CT image segmentation methods for bone used in medical additive manufacturing

The Reliability of CBCT to Assess Quality of Augmented Bone After Lateral Sinus Floor Elevation With Xenografts: A Retrospective Analysis

OBJETIVES

This study aimed to explore the reliability of cone beam computed tomography (CBCT) in evaluating the quality of augmented bone after lateral sinus floor elevation (LSFE) with xenografts.

MATERIALS AND METHODS

Thirty-six patients with lost maxillary molars were included, with half of whom received LSFE with xenografts and staged implant placement, and the other half showed no vertical bone defects and underwent implant placement directly. A total of 36 implants were included, with 18 implants in each group. A CBCT exam was taken before implant placement to acquire data on mineral quality at the future implant site, including bone mineral density (BMD), various microstructure indices, and gray values (GVs) within different threshold ranges. Augmented bone biopsies were collected during implant preparation. The microstructure indices and histological characteristics of the biopsies were evaluated by micro computed tomography (μCT) and histological staining. An implant-oriented volume of interest for CBCT analysis was established to co-locate the CBCT-measured data and the biopsy-related data using 3DSlicer. A Spearman rank correlation test was used to analyze the relationship between CBCT-measured data and the biopsy-related data.

RESULTS

μCT-measured microstructure indices of the augmented bone (BV/TV and Tb.Th) were significantly correlated with new bone area (BV/TV, p = 0.035, r = 0.498; Tb.Th, p = 0.027, r = 0.520). No correlation was found between the CBCT-measured and μCT-measured microstructure indices. CBCT-measured BMD and microstructure indices hardly showed any correlation with histological indices (p > 0.05). When the threshold was set from 0 to 50, the mean GVs were significantly, positively correlated with new bone area (p = 0.041, r = 0.486), and bone substitute area was positively correlated to the mean GVs of higher threshold (range 60-255, p = 0.048, r = 0.472; range 70-255, p = 0.009, r = 0.593).

CONCLUSIONS

CBCT without bone substitute segmentation was not reliable for evaluating the quality of xenogenic augmented bone after LSFE. The influence of the xenogenic substitute on CBCT analysis can be reduced by setting a low GV threshold. The bone substitute segmentation strategy may present a new way to increase the reliability of CBCT in evaluating xenogenic augmented bone.

Effect of hot water maceration, rehydration, and soft tissue presence on 3D geometry of bone

PURPOSE

In forensic medicine, maceration is often essential for examining bone surfaces, serving purposes such as identifying cut marks, making geometric measurements, and determining the victim's age. While hot water maceration removes soft tissue effectively, it is known to cause bone surface shrinkage. This raises the question of whether this effect is permanent or if it can be partially reversed through rehydration, considering the presence of soft tissue.

METHODS

Computed tomography (CT) scans were conducted on the radii of 20 paired human anatomic forearm specimens. Subsequently, the radii were extracted, macerated in 60 °C water, CT-scanned in an air environment, rehydrated, re-implanted into the forearms, and CT-scanned again.

RESULTS

Maceration resulted in a mean shrinkage of 0.12 mm on the outer bone surface. This shrinkage was nearly fully recoverable for the diaphysis after rehydration and accounting for soft tissue surrounding the bone. In contrast, the epiphysis showed permanent shrinkage, likely due to the loss of small bone fragments. Analysis of the inner bone surface indicated a smaller effect, but with significant standard deviations, especially for the epiphysis, possibly related to the less well-defined nature of the inner bone surface.

CONCLUSION

The epiphyseal surface of hot water-macerated bone will, on average, be approximately 0.15 mm deflated and cannot retain the original surface. On the other hand, the diaphyseal surface is less affected and can be nearly completely restored after rehydration and accounting for soft tissue surrounding the bone.

Minimal Detectable Bone Fracture Gaps in CT Images and Digital Three-Dimensional (3D) Radii Models

Knowledge of the minimal detectable bone fracture gap is essential in three-dimensional (3D) models, particularly in pre-operative planning of osteosynthesis to avoid overlooking gaps. In this study, defined incisions and bony displacements ranging from 100 to 400 µm were created in diaphyseal radii in 20 paired forearm specimens and verified with light microscopy. The specimens were scanned utilizing different computed tomography (CT) technologies/scanners, specimen positionings, scan protocols, image segmentations, and processing protocols. Inter- and intra-operator variabilities were reported as coefficient kappa. In CT images, fracture gaps of 100 µm and bone lamellae of 300 µm and 400 µm width were identified at a rate of 80 to 100%, respectively, independent of the investigated settings. In contrast, only 400µm incisions and bony displacements were visible in digital 3D models, with detection rates dependent on CT technology, image segmentation, and post-processing algorithm. 3D bone models based on state-of-the-art CT imaging can reliably visualize clinically relevant bone fracture gap sizes. However, verification of fractures to be surgically addressed should be verified with the original CT image series.

Segmentation of the iliac crest from CT-data for virtual surgical planning of facial reconstruction surgery using deep learning

BACKGROUND AND OBJECTIVES

For the planning of surgical procedures involving the bony reconstruction of the mandible, the autologous iliac crest graft, along with the fibula graft, has become established as a preferred donor region. While computer-assisted planning methods are increasingly gaining importance, the necessary preparation of geometric data based on CT imaging remains largely a manual process. The aim of this work was to develop and test a method for the automated segmentation of the iliac crest for subsequent reconstruction planning.

METHODS

A total of 1,398 datasets with manual segmentations were obtained as ground truth, with a subset of 400 datasets used for training and validation of the Neural Networks and another subset of 177 datasets used solely for testing. A deep Convolutional Neural Network implemented in a 3D U-Net architecture using Tensorflow was employed to provide a pipeline for automatic segmentation. Transfer learning was applied for model training optimization. Evaluation metrics included the Dice Similarity Coefficient, Symmetrical Average Surface Distance, and a modified 95% Hausdorff Distance focusing on regions relevant for transplantation.

RESULTS

The automated segmentation achieved high accuracy, with qualitative and quantitative assessments demonstrating predictions closely aligned with ground truths. Quantitative evaluation of the correspondence yielded values for geometric agreement in the transplant-relevant area of 92% +/- 7% (Dice coefficient) and average surface deviations of 0.605 +/- 0.41 mm. In all cases, the bones were identified as contiguous objects in the correct spatial orientation. The geometries of the iliac crests were consistently and completely recognized on both sides without any gaps.

CONCLUSIONS

The method was successfully used to extract the individual geometries of the iliac crest from CT data. Thus, it has the potential to serve as an essential starting point in a digitized planning process and to provide data for subsequent surgical planning. The complete automation of this step allows for efficient and reliable preparation of anatomical data for reconstructive surgeries.

Age estimation from median palatine suture using computed tomography reconstructed 3D images: a comparison of Northern and Southwestern Chinese populations

To investigate the potential of computed tomography (CT) images of median palatine suture (MP) for adult age estimation in the Northern and Southwestern Chinese populations. A total of 1110 cranial CT scans from individuals aged 10-79 years, including 557 northern Chinese and 553 southwestern Chinese, were collected for analysis. After volume reformation and multiplanar reconstruction, a total of 20 slices of median palatine suture were selected from each individual. The closure of sutures was analyzed into four stages, and the cumulative scores of 20 slices were recorded as the suture closure score (SCS). The correlations between SCS and age were compared among the two Chinese populations residing in diverse geographic regions. Regression models were established for age estimation. The estimation accuracy was evaluated based on the test set. The mean absolute error (MAE) and the correlation between predicted age and chronological age were calculated to evaluate estimation accuracy. The SCS of MP exhibited a significant correlation with age (0.613, northern male; 0.678, southwestern male; 0.730, northern female; 0.704, Southwestern female; 0.662, total). Furthermore, there were statistically significant differences in SCS among different regions and sex groups (p < 0.001). The cubic regression model had the highest R2 value in all subjects, especially among Northern females and Southwestern males, while the power and quadratic regression models showed the highest R2 value in Northern males and Southwestern females, respectively. In the test set, the Northern cohort demonstrated a lower MAE (9.06 ± 7.32 years, males; 9.17 ± 5.28 years, females) compared to the Southwestern cohort (9.19 ± 7.49 years, male; 10.61 ± 6.83 years, female). Additionally, it was observed that males exhibited a lower MAE than females in both regional groups. This study demonstrated the potential utility of CT images of the MP for age estimation in Chinese populations, emphasizing the significance of incorporating regional and sex factors within this context.

Kennedy class III and IV dental arches: Trueness analysis of digitization methods and 3D-printing step

This study aims to evaluate the trueness of Kennedy Class III and IV dental arches digitized by different methods and three-dimensionally (3D) printed using stereolithography technology in an in vitro setup. Reference casts (maxillary Kennedy class III and IV) were produced by computer assisted design and manufacture, and linearly measured at occlusocervical, interarch, and edentulous space dimensions. Intraoral scanner (IOS), extraoral scanner (EOS) and cone beam computed tomography (CBCT) digitized the reference casts. Each digital file was 3D-printed using stereolithography technology, totalizing sixty experimental casts (n=10 per group). The same measurements taken from the reference casts were performed on experimental casts. Two-way ANOVA and Bonferroni post-test were used for trueness (distortion between the experimental and reference casts). Distortion was significantly greater for class IV when compared with class III and increased after the 3D-printing step. Among digitizing methods, IOS and EOS had a similar performance and casts from CBCT showed higher distortion, reaching -1.0 and -1.4 mm in the edentulous spaces of digital and 3D-printed cast, respectively. It was possible to conclude that the trueness of Kennedy class III and IV arches were different according to digitizing processes with higher distortion at the edentulous spaces when the cast was digitized by CBCT and converted to a 3D model, compared to IOS and EOS; and in the Kennedy class IV dental arch condition.

AFSegNet: few-shot 3D ankle-foot bone segmentation via hierarchical feature distillation and multi-scale attention and fusion

Accurate segmentation of ankle and foot bones from CT scans is essential for morphological analysis. Ankle and foot bone segmentation challenges due to the blurred bone boundaries, narrow inter-bone gaps, gaps in the cortical shell, and uneven spongy bone textures. Our study endeavors to create a deep learning framework that harnesses advantages of 3D deep learning and tackles the hurdles in accurately segmenting ankle and foot bones from clinical CT scans. A few-shot framework AFSegNet is proposed considering the computational cost, which comprises three 3D deep-learning networks adhering to the principles of progressing from simple to complex tasks and network structures. Specifically, a shallow network first over-segments the foreground, and along with the foreground ground truth are used to supervise a subsequent network to detect the over-segmented regions, which are overwhelmingly inter-bone gaps. The foreground and inter-bone gap probability map are then input into a network with multi-scale attentions and feature fusion, a loss function combining region-, boundary-, and topology-based terms to get the fine-level bone segmentation. AFSegNet is applied to the 16-class segmentation task utilizing 123 in-house CT scans, which only requires a GPU with 24 GB memory since the three sub-networks can be successively and individually trained. AFSegNet achieves a Dice of 0.953 and average surface distance of 0.207. The ablation study and comparison with two basic state-of-the-art networks indicates the effectiveness of the progressively distilled features, attention and feature fusion modules, and hybrid loss functions, with the mean surface distance error decreased up to 50 %.

BounTI (boundary-preserving threshold iteration): A user-friendly tool for automatic hard tissue segmentation

X-ray Computed Tomography (CT) images are widely used in various fields of natural, physical, and biological sciences. 3D reconstruction of the images involves segmentation of the structures of interest. Manual segmentation has been widely used in the field of biological sciences for complex structures composed of several sub-parts and can be a time-consuming process. Many tools have been developed to automate the segmentation process, all with various limitations and advantages, however, multipart segmentation remains a largely manual process. The aim of this study was to develop an open-access and user-friendly tool for the automatic segmentation of calcified tissues, specifically focusing on craniofacial bones. Here we describe BounTI, a novel segmentation algorithm which preserves boundaries between separate segments through iterative thresholding. This study outlines the working principles behind this algorithm, investigates the effect of several input parameters on its outcome, and then tests its versatility on CT images of the craniofacial system from different species (e.g. a snake, a lizard, an amphibian, a mouse and a human skull) with various scan qualities. The case studies demonstrate that this algorithm can be effectively used to segment the craniofacial system of a range of species automatically. High-resolution microCT images resulted in more accurate boundary-preserved segmentation, nonetheless significantly lower-quality clinical images could still be segmented using the proposed algorithm. Methods for manual intervention are included in this tool when the scan quality is insufficient to achieve the desired segmentation results. While the focus here was on the craniofacial system, BounTI can be used to automatically segment any hard tissue. The tool presented here is available as an Avizo/Amira add-on, a stand-alone Windows executable, and a Python library. We believe this accessible and user-friendly segmentation tool can benefit the wider anatomical community.

Influence of postprinting cleaning methods on the cleaning efficiency and surface and mechanical properties of three-dimensionally printed resins

STATEMENT OF PROBLEM

The outcome of photopolymerized 3-dimensional (3D) printing is influenced by the methods used for postprinting cleaning, yet information on postprinting cleaning is sparse.

PURPOSE

The purpose of this in vitro study was to assess the cleaning efficiency and surface and mechanical properties of 3D printed resin according to postprinting cleaning methods.

MATERIAL AND METHODS

Specimens were fabricated from a 3D model using resin materials (NextDent C&B MFH and DIOnavi-P. MAX) and were tested for postprinting cleaning methods for 5 minutes with isopropyl alcohol, isopropyl alcohol + ultrasonic, ethyl alcohol, ethyl alcohol + ultrasonic, and ultrasonic alone. Postpolymerization was followed for 5 minutes. The cleaning efficiency, microcomputed tomography (µCT), surface roughness, Vickers hardness, and flexural strength of the specimens were evaluated. The 1-way ANOVA test was performed after considering normality. A post hoc analysis with Bonferroni was also performed (α=.008 or.005).

RESULTS

Ultrasonic in addition to cleaning solutions significantly improved the cleaning efficiency in NextDent C&B MFH specimens (P<.005), whereas ultrasonic did not affect the efficiency in DIOnavi-P. MAX specimens. No significant differences were found in surface roughness by postprinting cleaning methods in either NextDent C&B MFH or DIOnavi-P. MAX (P>.005). No significant changes in surface hardness were observed by postprinting cleaning methods (P>.008). In the NextDent C&B MFH, ethyl alcohol + ultrasonic significantly decreased the flexural strength (P<.005). There were no significant differences in the flexural strength in the DIOnavi-P. MAX (P>.005).

CONCLUSIONS

Ethyl alcohol was comparable with isopropyl alcohol for use as a postprinting cleaning solution for both NextDent C&B MFH and DIOnavi-P. MAX. The addition of ultrasonic to cleaning solutions should be applied with caution. These findings suggest that different postprinting cleaning methods can be recommended depending on the 3D printed resin materials.

Accuracy Analysis of 3D Bone Fracture Models: Effects of Computed Tomography (CT) Imaging and Image Segmentation

The introduction of three-dimensional (3D) printed anatomical models has garnered interest in pre-operative planning, especially in orthopedic and trauma surgery. Identifying potential error sources and quantifying their effect on the model dimensional accuracy are crucial for the applicability and reliability of such models. In this study, twenty radii were extracted from anatomic forearm specimens and subjected to osteotomy to simulate a defined fracture of the distal radius (Colles' fracture). Various factors, including two different computed tomography (CT) technologies (energy-integrating detector (EID) and photon-counting detector (PCD)), four different CT scanners, two scan protocols (i.e., routine and high dosage), two different scan orientations, as well as two segmentation algorithms were considered to determine their effect on 3D model accuracy. Ground truth was established using 3D reconstructions of surface scans of the physical specimens. Results indicated that all investigated variables significantly impacted the 3D model accuracy (p < 0.001). However, the mean absolute deviation fell within the range of 0.03 ± 0.20 to 0.32 ± 0.23 mm, well below the 0.5 mm threshold necessary for pre-operative planning. Intra- and inter-operator variability demonstrated fair to excellent agreement for 3D model accuracy, with an intra-class correlation (ICC) of 0.43 to 0.92. This systematic investigation displayed dimensional deviations in the magnitude of sub-voxel imaging resolution for all variables. Major pitfalls included missed or overestimated bone regions during the segmentation process, necessitating additional manual editing of 3D models. In conclusion, this study demonstrates that 3D bone fracture models can be obtained with clinical routine scanners and scan protocols, utilizing a simple global segmentation threshold, thereby providing an accurate and reliable tool for pre-operative planning.

Patient-matched osseointegrated prostheses for thumb amputees: a cadaver and feasibility study

Thumb amputations affect 50% of hand functionality. Common solutions consist of microsurgical treatments or silicone vacuum prosthesis. Not all patients are eligible for microsurgical treatment and the use of vacuum prosthesis is often discouraged because of their instability. On the contrary, osseointegrated prosthesis provide stable retention and osseoperception. This cadaveric study evaluated the process of a patient-matched osseointegrated prosthesis for the treatment of thumb amputees. Computed tomography (CT) medical images reconstruction provided information on metacarpal stump, used as input for the parametric screw design. Preoperative planning guided the surgeons in the surgery: postoperative placement confirmed the accuracy of the preoperative planning. Surgeons were directly involved in the implant design to meet their requirements and patient needs. Implants were inserted into cadaveric specimens in one-stage surgery. A similar process can be adopted and exploited for the treatment of different levels of thumb amputations and long finger amputations.

Computer-Assisted Evaluation Confirms Spontaneous Healing of Donor Site One Year following Bone Block Harvesting from Mandibular Retromolar Region-A Cohort Study

Bone augmentation prior to dental implant placement is a common scenario in the dental implantology field. Among the important intraoral harvesting sites to obtain bone blocks is the ramus/retromolar region that has a high success rate and long-lasting alveolar ridge augmentation. Preserving the bone volume and quality at the donor site is crucial for preventing further complications or to serve as a site for re-harvesting. Healing of the intraoral donor sites has been described in the maxillofacial field. This study aimed to evaluate the spontaneous healing of the mandibular retromolar donor site utilizing computer-assisted quantification 6 and 12 months after bone harvesting.

MATERIALS AND METHODS

The study was conducted on patients who underwent an alveolar ridge augmentation using an intraoral retromolar bone graft. Three CBCT scans were performed-intraoperative, and at six months and one year after the surgical procedure. By using the Materialise Mimics Innovation Suite software 26.0 features segmentation by thresholding, Hounsfield unit averaging, and superimposition of the tomographies, we could precisely quantify the healing process utilizing spatial and characteristic measures.

RESULTS

In all cases, the computer-aided quantification showed that six months following surgery, the donor site had recovered up to 64.5% ± 4.24 of its initial volume, and this recovery increased to 89.2% ± 2.6 after one year. Moreover, the Hounsfield unit averaging confirmed dynamic bone quality healing, starting at 690.3 ± 81 HU for the bone block, decreasing to 102 ± 27.8 HU at six months postoperatively, and improving to 453.9 ± 91.4 HU at the donor site after a year.

CONCLUSIONS

This study demonstrates that there is no need for additional replanting at the donor site following retromolar bone block harvesting, whether autogenous or allograft, since spontaneous healing occurs 12 months following the surgery.

Biomedical image segmentation algorithm based on dense atrous convolution

Biomedical images have complex tissue structures, and there are great differences between images of the same part of different individuals. Although deep learning methods have made some progress in automatic segmentation of biomedical images, the segmentation accuracy is relatively low for biomedical images with significant changes in segmentation targets, and there are also problems of missegmentation and missed segmentation. To address these challenges, we proposed a biomedical image segmentation method based on dense atrous convolution. First, we added a dense atrous convolution module (DAC) between the encoding and decoding paths of the U-Net network. This module was based on the inception structure and atrous convolution design, which can effectively capture multi-scale features of images. Second, we introduced a dense residual pooling module to detect multi-scale features in images by connecting residual pooling blocks of different sizes. Finally, in the decoding part of the network, we adopted an attention mechanism to suppress background interference by enhancing the weight of the target area. These modules work together to improve the accuracy and robustness of biomedical image segmentation. The experimental results showed that compared to mainstream segmentation networks, our segmentation model exhibited stronger segmentation ability when processing biomedical images with multiple-shaped targets. At the same time, this model can significantly reduce the phenomenon of missed segmentation and missegmentation, improve segmentation accuracy, and make the segmentation results closer to the real situation.

Dimensional accuracy and precision and surgeon perception of additively manufactured bone models: effect of manufacturing technology and part orientation

BACKGROUND

Additively manufactured (AM) anatomical bone models are primarily utilized for training and preoperative planning purposes. As such, they must meet stringent requirements, with dimensional accuracy being of utmost importance. This study aimed to evaluate the precision and accuracy of anatomical bone models manufactured using three different AM technologies: digital light processing (DLP), fused deposition modeling (FDM), and PolyJetting (PJ), built in three different part orientations. Additionally, the study sought to assess surgeons' perceptions of how well these models mimic real bones in simulated osteosynthesis.

METHODS

Computer-aided design (CAD) models of six human radii were generated from computed tomography (CT) imaging data. Anatomical models were then manufactured using the three aforementioned technologies and in three different part orientations. The surfaces of all models were 3D-scanned and compared with the original CAD models. Furthermore, an anatomical model of a proximal femur including a metastatic lesion was manufactured using the three technologies, followed by (mock) osteosynthesis performed by six surgeons on each type of model. The surgeons' perceptions of the quality and haptic properties of each model were assessed using a questionnaire.

RESULTS

The mean dimensional deviations from the original CAD model ranged between 0.00 and 0.13 mm with maximal inaccuracies < 1 mm for all models. In surgical simulation, PJ models achieved the highest total score on a 5-point Likert scale ranging from 1 to 5 (with 1 and 5 representing the lowest and highest level of agreement, respectively), (3.74 ± 0.99) in the surgeons' perception assessment, followed by DLP (3.41 ± 0.99) and FDM (2.43 ± 1.02). Notably, FDM was perceived as unsuitable for surgical simulation, as the material melted during drilling and sawing.

CONCLUSIONS

In conclusion, the choice of technology and part orientation significantly influenced the accuracy and precision of additively manufactured bone models. However, all anatomical models showed satisfying accuracies and precisions, independent of the AM technology or part orientation. The anatomical and functional performance of FDM models was rated by surgeons as poor.

3D osteotomies-improved accuracy with patient-specific instruments (PSI)

PURPOSE

Three-dimensional (3D) printed patient-specific instruments (PSI) have been introduced to increase precision and simplify surgical procedures. Initial results in femoral and tibial osteotomies are promising, but validation studies on 3D planning, manufacturing of patient-specific cutting blocks and 3D evaluation of the attained results are lacking.

METHODS

In this study, patient-specific cutting blocks and spacers were designed, fabricated, and used to perform a high tibial osteotomy (HTO). After segmentation of CT data sets from 13 human tibiae, 3D digital planning of the HTO was performed with a medial opening of 8 mm. These 3D models were used to fabricate patient-specific cutting blocks and spacers. After the surgical procedure, accuracy was evaluated measuring 3D joint angles and surface deviations.

RESULTS

The lowest mean deviation was found to be 0.57° (SD ± 0.27) for the MPTA. Medial and lateral tibial slope deviated from the 3D planning by an average of 0.98° (SD ± 0.53) and 1.26° (SD ± 0.79), respectively, while tibial torsion deviated by an average of 5.74° (SD ± 3.24). Color analysis of surface deviations showed excellent and good agreement in 7 tibiae.

CONCLUSION

With 3D cutting blocks and spacers, the 3D planning of the HTO can be translated into reality with small deviations of the resulting joint angles. Within this study, the results of the individual steps are examined for errors and thus a critical evaluation of this new and promising method for performing patient-specific HTOs is presented.

Imaging in revision total knee arthroplasty: A novel 3D classification system for tibial bone defects

PURPOSE

The primary purpose of the study was to use pre-revision total knee arthroplasty (TKA) computer-tomography (CT)-images to analyse typical tibial bone defects and create a new schematic three-dimensional (3D)-classification system. The secondary purpose was to investigate the association between defect size and implant selection at the time of revision surgery.

METHODS

Eighty-four patients with preoperative CT-scans underwent revision of a primary TKA. CT-image segmentation with the 3D-Slicer Software was performed retrospectively, and a new three-dimensional classification system was used to grade tibial bone defects. The location of tibial bone defects was recorded for all cases. Volumetric 3D bone defect measurements were used to investigate the association between the bone defect volume, the indication for rTKA, and the use of modular revision components. The t-test, the Mann-Whitney-U test, and the Fisher's exact-test were used for group comparisons, and the Kruskal-Wallis test was used for multiple group comparisons.

RESULTS

The most common anatomic regions for both contained and uncontained tibial bone defects were the anteromedial epiphysis (N = 50; mean epiphyseal-defect: 5.9 cm³) and metaphysis (N = 15; mean metaphyseal-defect: 9.6 cm³). A significant association was found between patients with preoperative metaphyseal defects (N = 22) and the use of tibial augments (N = 7) (p = 0.04). The use of cones/sleeves was associated with a significantly increased 3D-CT volume of the preoperative metaphyseal bone defects (p = 0.04). Patients with osteoporosis had significantly larger volumetric defects in the metaphysis (p = 0.01).

CONCLUSION

Our results emphasise the importance of considering the three-dimensional nature of tibial defects in rTKA. The findings suggest that an understanding of the volume of the defect size through CT imaging can predict the need for augments and cones/sleeves and, especially in patients with osteoporosis can help the surgeon identify larger metaphyseal defects and ensure optimal metaphyseal fixation through appropriate implant selection.

LEVEL OF EVIDENCE

Level III, retrospective cohort study.

Increased subtalar rotational motion in patients with symptomatic ankle instability under load and stress conditions

PURPOSE

Differentiating subtalar and ankle instability in the clinical setting is challenging. This study aims to analyze the rotational laxity of the subtalar joint bilaterally in patients with asymptomatic and symptomatic ankle instability under simulated load and stress-induced position of the subtalar joint.

METHODS

A case-control study was conducted using an adjustable load device (ALD). Patients with chronic ankle instability and healthy volunteers were included. Each subject underwent a CT scan under mechanical stress and simulated weight-bearing conditions, maintaining maximum eversion and inversion hindfoot positions. The images were obtained in a single model, allowing calculations of the motion vector as well as the helical axis. The helical axis was defined by a rotation angle and a translation distance.

RESULTS

A total of 72 feet were included in the study. Thirty-one patients with unilateral symptoms and five healthy controls were selected, defining two groups: symptomatic (n = 31) and asymptomatic (n = 41). An absolute difference of 4.6º (95%CI 2-11.1) rotation angle was found on the helical axis of the symptomatic vs. asymptomatic group (p = 0.001). No significant differences were detected in the translation distance (n.s.) between the groups. Additionally, a significant positive correlation was found between the rotation angle and translation distance through the helical axis in the asymptomatic group (r = 0.397, p = 0.027).

CONCLUSION

Patients with chronic ankle instability suspected of having subtalar joint instability showed a wider subtalar range of laxity in terms of rotation about the helical axis. Furthermore, differences in kinematics between symptomatic and asymptomatic hindfeet was demonstrated when both feet were compared.

LEVEL OF EVIDENCE

III.

Tibial bone defect prediction based on preoperative artefact-reduced CT imaging is superior to standard radiograph assessment

PURPOSE

The purpose of this study was to evaluate the accuracy of preoperative CT-based Anderson Orthopaedic Research Institute (AORI)-grading and to correlate Computed tomography (CT)-based volumetric defect measurements with intraoperative AORI findings.

METHODS

99 patients undergoing revision total knee arthroplasty (rTKA) with preoperative CT-images were identified in an institutional revision registry. CT-image segmentation with 3D-Slicer Software was used to create 3D tibial bone defects which were then graded according to the AORI-classification. The AORI classification categorizes tibial defects into three types: Type I has healthy cortical and cancellous bone near the joint line, Type II involves metaphyseal bone loss affecting one or both condyles, and Type III indicates deficient metaphyseal bone with distal defects and potential damage to the patellar tendon and collateral ligament attachments. These 3D-CT gradings were compared to preoperative X-ray and intraoperative AORI grading. The Friedman test was used to investigate differences between AORI values of each measurement method. Volumetric 3D-bone defect measurements were used to investigate the relationship between AORI classification and volumetric defect size in the three anatomic zones of the tibia.

RESULTS

Substantial agreements between preoperative 3D-CT AORI and intraoperative AORI (kappa = 0.663; P < 0.01) and fair agreements between preoperative X-ray AORI and intraoperative AORI grading (kappa = 0.304; P < 0.01) were found. Moderate correlations between volume of remaining bone and intraoperative AORI grading were found in epiphysis (rS = - 0.529; P < 0.001), metaphysis (rS = - 0.557; P < 0.001) and diaphysis (rS = - 0.421; P < 0.001). Small volumetric differences between AORI I vs. AORI II defects and relatively large differences between AORI II and AORI III defects in each zone were detected.

CONCLUSION

Tibial bone defect prediction based on preoperative 3D-CT segmentation showed a substantial agreement with intraoperative findings and is superior to standard radiograph assessment. The relatively small difference in defect volume between AORI I, IIa and IIb suggests that updated CT-based classifications might hold benefits for the planning of rTKA.

LEVEL OF EVIDENCE

Retrospective Cohort Study; III.

Hindfoot motion through helical axis image-based on dynamic CT scan using an original simulated weightbearing device

BACKGROUND

Determining the treatment of subtalar joint (STJ) instability requires a better understanding of the biomechanical principles underlying the condition and, a proper diagnosis. This study aimed to analyze "in vivo" the range of motion of the subtalar joint (STJ) measured on two (2D) and three dimensions (3D) image-based on CT Scan using an original device that maintains a simulated weightbearing. The secondary goal was to correlate the 2D and 3D measurement.

METHODS

An observational study was conducted, using an original Dynamic Simulated Weightbearing Device. Asymptomatic ankles were included. Each subject underwent a CT scan under mechanical stress and simulated weightbearing conditions, maintaining maximum eversion and inversion hindfoot positions. The images were obtained, combining both inversion and eversion positions in a single model, which allows for to calculation of the motion vector as well as the helical axis. The helical axis (rotation angle and translation distance), subtalar tilt, anterior drawer, and, subtalar and calcaneocuboid uncoverage were the determinations.

RESULTS

Forty asymptomatic ankles were included. The average range of motion of the STJ amounts to 31.5° ± 9.1° of rotation and 1.56 ± 0.8 mm of translation distance. The anterior drawer and subtalar uncoverage variables were statistically significantly related to each other (r = 0.57; P = 0.00001). However, these 2-D measured variables were not related to kinematic measures of rotation through the helical axis (3D) (p = 0.14; p = 0.19) CONCLUSIONS: The average range of motion of the STJ amounts to 31.5° ± 9.1° of rotation and 1.56 ± 0.8 mm of translation distance. We found no significant correlation between 2D and 3D measurements. In our opinion, the rotation angle and translation distance should be considered the most accurate measurements and should be calculated on every STJ instability for comparison with the asymptomatic population LEVEL OF EVIDENCE: Observational study.

LEVEL OF EVIDENCE III

The impact of teeth and dental restorations on gray value distribution in cone-beam computer tomography: a pilot study

PURPOSE

To investigate the influence of teeth and dental restorations on the facial skeleton's gray value distributions in cone-beam computed tomography (CBCT).

METHODS

Gray value selection for the upper and lower jaw segmentation was performed in 40 patients. In total, CBCT data of 20 maxillae and 20 mandibles, ten partial edentulous and ten fully edentulous in each jaw, respectively, were evaluated using two different gray value selection procedures: manual lower threshold selection and automated lower threshold selection. Two sample t tests, linear regression models, linear mixed models, and Pearson's correlation coefficients were computed to evaluate the influence of teeth, dental restorations, and threshold selection procedures on gray value distributions.

RESULTS

Manual threshold selection resulted in significantly different gray values in the fully and partially edentulous mandible. (p = 0.015, difference 123). In automated threshold selection, only tendencies to different gray values in fully edentulous compared to partially edentulous jaws were observed (difference: 58-75). Significantly different gray values were evaluated for threshold selection approaches, independent of the dental situation of the analyzed jaw. No significant correlation between the number of teeth and gray values was assessed, but a trend towards higher gray values in patients with more teeth was noted.

CONCLUSIONS

Standard gray values derived from CT imaging do not apply for threshold-based bone segmentation in CBCT. Teeth influence gray values and segmentation results. Inaccurate bone segmentation may result in ill-fitting surgical guides produced on CBCT data and misinterpreting bone density, which is crucial for selecting surgical protocols. Created with BioRender.com.

A biomechanical study of osseointegrated patient-matched additively manufactured implant for treatment of thumb amputees

Thumb amputations leads to 50 % loss in hand functionality. To date, silicone vacuum prosthesis and autologous transplantation are the most adopted treatment solutions: nevertheless, vacuum prostheses lack in stability and cause skin issue and surgical treatment is not always accepted by patients. Osseointegrated implants were demonstrated to enhance stability, restore osseoperception and increase the time of prosthesis use. Thumb amputations present varying stump sizes: a standard size implant cannot address specificity of each patient, while a patient matched solution can meet surgeon requirements, by geometrical features of implant. The fixture presented in the current paper is the first additively manufactured patient matched osseointegrated implant for the treatment of thumb amputees. The current work aims to verify and validate a predictive finite element model (FEM) for mechanical strength of the presented fixture. FEM was demonstrated to correctly evaluate the mechanical strength of patient matched device. Minimum strength requirements were calculated in different core diameters: FEM were experimentally validated. Safety factor of 1.5 was guaranteed. Finally, considerations on performance of the prototype were carried out by means of insertion tests in Sawbones and axial pull-out force assessment. Cadaver tests to evaluate the entire procedure and production process are ongoing.

Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks

Difficulty in detecting tumours in early stages is the major cause of mortalities in patients, despite the advancements in treatment and research regarding ovarian cancer. Deep learning algorithms were applied to serve the purpose as a diagnostic tool and applied to CT scan images of the ovarian region. The images went through a series of pre-processing techniques and, further, the tumour was segmented using the UNet model. The instances were then classified into two categories-benign and malignant tumours. Classification was performed using deep learning models like CNN, ResNet, DenseNet, Inception-ResNet, VGG16 and Xception, along with machine learning models such as Random Forest, Gradient Boosting, AdaBoosting and XGBoosting. DenseNet 121 emerges as the best model on this dataset after applying optimization on the machine learning models by obtaining an accuracy of 95.7%. The current work demonstrates the comparison of multiple CNN architectures with common machine learning algorithms, with and without optimization techniques applied.

3D Printing Approaches to Engineer Cardiac Tissue

PURPOSE OF REVIEW

Bioengineering of functional cardiac tissue composed of primary cardiomyocytes has great potential for myocardial regeneration and in vitro tissue modeling. 3D bioprinting was developed to create cardiac tissue in hydrogels that can mimic the structural, physiological, and functional features of native myocardium. Through a detailed review of the 3D printing technologies and bioink materials used in the creation of a heart tissue, this article discusses the potential of engineered heart tissues in biomedical applications.

RECENT FINDINGS

In this review, we discussed the recent progress in 3D bioprinting strategies for cardiac tissue engineering, including bioink and 3D bioprinting methods as well as examples of engineered cardiac tissue such as in vitro cardiac models and vascular channels. 3D printing is a powerful tool for creating in vitro cardiac tissues that are structurally and functionally similar to real tissues. The use of human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) enables the generation of patient-specific tissues. These tissues have the potential to be used for regenerative therapies, disease modeling, and drug testing.

Application of 3D Printing Technology in the Treatment of Hoffa's Fracture Nonunion

Objective  To evaluate a proposed three-dimensional (3D) printing process of a biomodel developed with the aid of fused deposition modeling (FDM) technology based on computed tomography (CT) scans of an individual with nonunion of a coronal femoral condyle fracture (Hoffa's fracture). Materials and Methods  Thus, we used CT scans, which enable the evaluation of the 3D volumetric reconstruction of the anatomical model, as well as of the architecture and bone geometry of sites with complex anatomy, such as the joints. In addition, it enables the development of the virtual surgical planning (VSP) in a computer-aided design (CAD) software. This technology makes it possible to print full-scale anatomical models that can be used in surgical simulations for training and in the choice of the best placement of the implant according to the VSP. In the radiographic evaluation of the osteosynthesis of the Hoffa's fracture nonunion, we assessed the position of the implant in the 3D-printed anatomical model and in the patient's knee. Results  The 3D-printed anatomical model showed geometric and morphological characteristics similar to those of the actual bone. The position of the implants in relation to the nonunion line and anatomical landmarks showed great accuracy in the comparison of the patient's knee with the 3D-printed anatomical model. Conclusion  The use of the virtual anatomical model and the 3D-printed anatomical model with the additive manufacturing (AM) technology proved to be effective and useful in planning and performing the surgical treatment of Hoffa's fracture nonunion. Thus, it showed great accuracy in the reproducibility of the virtual surgical planning and the 3D-printed anatomical model.

Advantages of a Training Course for Surgical Planning in Virtual Reality for Oral and Maxillofacial Surgery: Crossover Study

BACKGROUND

As an integral part of computer-assisted surgery, virtual surgical planning (VSP) leads to significantly better surgery results, such as for oral and maxillofacial reconstruction with microvascular grafts of the fibula or iliac crest. It is performed on a 2D computer desktop screen (DS) based on preoperative medical imaging. However, in this environment, VSP is associated with shortcomings, such as a time-consuming planning process and the requirement of a learning process. Therefore, a virtual reality (VR)-based VSP application has great potential to reduce or even overcome these shortcomings due to the benefits of visuospatial vision, bimanual interaction, and full immersion. However, the efficacy of such a VR environment has not yet been investigated.

OBJECTIVE

This study aimed to demonstrate the possible advantages of a VR environment through a substep of VSP, specifically the segmentation of the fibula (calf bone) and os coxae (hip bone), by conducting a training course in both DS and VR environments and comparing the results.

METHODS

During the training course, 6 novices were taught how to use a software application in a DS environment (3D Slicer) and in a VR environment (Elucis) for the segmentation of the fibula and os coxae, and they were asked to carry out the maneuvers as accurately and quickly as possible. Overall, 13 fibula and 13 os coxae were segmented for each participant in both methods (VR and DS), resulting in 156 different models (78 fibula and 78 os coxae) per method (VR and DS) and 312 models in total. The individual learning processes in both environments were compared using objective criteria (time and segmentation performance) and self-reported questionnaires. The models resulting from the segmentation were compared mathematically (Hausdorff distance and Dice coefficient) and evaluated by 2 experienced radiologists in a blinded manner.

RESULTS

A much faster learning curve was observed for the VR environment than the DS environment (β=.86 vs β=.25). This nearly doubled the segmentation speed (cm³/min) by the end of training, leading to a shorter time (P<.001) to reach a qualitative result. However, there was no qualitative difference between the models for VR and DS (P=.99). The VR environment was perceived by participants as more intuitive and less exhausting, and was favored over the DS environment.

CONCLUSIONS

The more rapid learning process and the ability to work faster in the VR environment could save time and reduce the VSP workload, providing certain advantages over the DS environment.

Additive Manufacturing of 3D Anatomical Models-Review of Processes, Materials and Applications

The methods of additive manufacturing of anatomical models are widely used in medical practice, including physician support, education and planning of treatment procedures. The aim of the review was to identify the area of additive manufacturing and the application of anatomical models, imitating both soft and hard tissue. The paper outlines the most commonly used methodologies, from medical imaging to obtaining a functional physical model. The materials used to imitate specific organs and tissues, and the related technologies used to produce, them are included. The study covers publications in English, published by the end of 2022 and included in the Scopus. The obtained results emphasise the growing popularity of the issue, especially in the areas related to the attempt to imitate soft tissues with the use of low-cost 3D printing and plastic casting techniques.

3D Bioprinting tissue analogs: Current development and translational implications

Three-dimensional (3D) bioprinting is a promising and rapidly evolving technology in the field of additive manufacturing. It enables the fabrication of living cellular constructs with complex architectures that are suitable for various biomedical applications, such as tissue engineering, disease modeling, drug screening, and precision regenerative medicine. The ultimate goal of bioprinting is to produce stable, anatomically-shaped, human-scale functional organs or tissue substitutes that can be implanted. Although various bioprinting techniques have emerged to develop customized tissue-engineering substitutes over the past decade, several challenges remain in fabricating volumetric tissue constructs with complex shapes and sizes and translating the printed products into clinical practice. Thus, it is crucial to develop a successful strategy for translating research outputs into clinical practice to address the current organ and tissue crises and improve patients' quality of life. This review article discusses the challenges of the existing bioprinting processes in preparing clinically relevant tissue substitutes. It further reviews various strategies and technical feasibility to overcome the challenges that limit the fabrication of volumetric biological constructs and their translational implications. Additionally, the article highlights exciting technological advances in the 3D bioprinting of anatomically shaped tissue substitutes and suggests future research and development directions. This review aims to provide readers with insight into the state-of-the-art 3D bioprinting techniques as powerful tools in engineering functional tissues and organs.

A densely connected LDCT image denoising network based on dual-edge extraction and multi-scale attention under compound loss

BACKGROUND

Low dose computed tomography (LDCT) uses lower radiation dose, but the reconstructed images contain higher noise that can have negative impact in disease diagnosis. Although deep learning with the edge extraction operators reserves edge information well, only applying the edge extraction operators to input LDCT images does not yield overall satisfactory results.

OBJECTIVE

To improve LDCT images quality, this study proposes and tests a dual edge extraction multi-scale attention mechanism convolution neural network (DEMACNN) based on a compound loss.

METHODS

The network uses edge extraction operators to extract edge information from both the input images and the feature maps in the network, improving the utilization of the edge operators and retaining the images edge information. The feature enhancement block is constructed by fusing the attention mechanism and multi-scale module, enhancing effective information, while suppressing useless information. The residual learning method is used to learn the network, improving the performance of the network, and solving the problem of gradient disappearance. Except for the network structure, a compound loss function, which consists of the MSE loss, the proposed joint total variation loss, and the edge loss, is proposed to enhance the denoising ability of the network and reserve the edge of images.

RESULTS

Compared with other advanced methods (REDCNN, CT-former and EDCNN), the proposed new network achieves the best PSNR and SSIM values in LDCT images of the abdomen, which are 33.3486 and 0.9104, respectively. In addition, the new network also performs well on head and chest image data.

CONCLUSION

The experimental results demonstrate that the proposed new network structure and denoising algorithm not only effectively removes the noise in LDCT images, but also protects the edges and details of the images well.

Distance mapping in three-dimensional virtual surgical planning in hand, wrist and forearm surgery: a tool to avoid mistakes

PURPOSE

Three-dimensional planning in corrective surgeries in the hand and wrist has become popular throughout the last 20 years. Imaging technologies and software have improved since their first description in the late 1980s. New imaging technologies, such as distance mapping (DM), improve the safety of virtual surgical planning (VSP) and help to avoid mistakes. We describe the effective use of DM in two representative and frequently performed surgical interventions (radius malunion and scaphoid pseudoarthrosis).

METHODS

We simulated surgical intervention in both cases using DM. Joint spaces were quantitatively and qualitatively displayed in a colour-coded fashion, which allowed the estimation of cartilage thickness and joint space congruency. These parameters are presented in the virtual surgical planning pre- and postoperatively as well as in the actual situation in our cases.

RESULTS

DM had a high impact on the VSP, especially in radius corrective osteotomy, where we changed the surgical plan due to the visualization of the planned postoperative situation. The actual postoperative situation was also documented using DM, which allowed for comparison of the VSP and the achieved postoperative situation. Both patients were successfully treated, and bone healing and clinical improvement were achieved.

CONCLUSION

The use of colour-coded static or dynamic distance mapping is useful for virtual surgical planning of corrective osteotomies of the hand, wrist and forearm. It also allows confirmation of the correct patient treatment and assessment of the follow-up radiological documentation.

An Overview of 3D Anatomical Model Printing in Orthopedic Trauma Surgery

Introduction

3D object printing technology is a resource increasingly used in medicine in recent years, mainly incorporated in surgical areas like orthopedics. The models made by 3D printing technology provide surgeons with an accurate analysis of complex anatomical structures, allowing the planning, training, and surgery simulation. In orthopedic surgery, this technique is especially applied in oncological surgeries, bone, and joint reconstructions, and orthopedic trauma surgeries. In these cases, it is possible to prototype anatomical models for surgical planning, simulating, and training, besides printing of instruments and implants.

Purpose

The purpose of this paper is to describe the acquisition and processing from computed tomography images for 3D printing, to describe modeling and the 3D printing process of the biomodels in real size. This paper highlights 3D printing with the applicability of the 3D biomodels in orthopedic surgeries and shows some examples of surgical planning in orthopedic trauma surgery.

Patients and Methods

Four examples were selected to demonstrate the workflow and rationale throughout the process of planning and printing 3D models to be used in a variety of situations in orthopedic trauma surgeries. In all cases, the use of 3D modeling has impacted and improved the final treatment strategy.

Conclusion

The use of the virtual anatomical model and the 3D printed anatomical model with the additive manufacturing technology proved to be effective and useful in planning and performing the surgical treatment of complex articular fractures, allowing surgical planning both virtual and with the 3D printed anatomical model, besides being useful during the surgical time as a navigation instrument.

Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients

Recently, Artificial Intelligence (AI) and Machine Learning (ML) have been successfully applied to many domains of interest including medical diagnosis. Due to the availability of a large quantity of data, it is possible to build reliable AI systems that assist humans in making decisions. The recent Covid-19 pandemic quickly spread over the world causing serious health problems and severe economic and social damage. Computer scientists are actively working together with doctors on different ML models to diagnose Covid-19 patients using Computed Tomography (CT) scans and clinical data. In this work, we propose a neural-symbolic system that predicts if a Covid-19 patient arriving at the hospital will end in a critical condition. The proposed system relies on Deep 3D Convolutional Neural Networks (3D-CNNs) for analyzing lung CT scans of Covid-19 patients, Decision Trees (DTs) for predicting if a Covid-19 patient will eventually pass away by analyzing its clinical data, and a neural system that integrates the previous ones using Hierarchical Probabilistic Logic Programs (HPLPs). Predicting if a Covid-19 patient will end in a critical condition is useful for managing the limited number of intensive care at the hospital. Moreover, knowing early that a Covid-19 patient could end in serious conditions allows doctors to gain early knowledge on patients and provide special treatment to those predicted to finish in critical conditions. The proposed system, entitled Neural HPLP, obtains good performance in terms of area under the receiver operating characteristic and precision curves with values of about 0.96 for both metrics. Therefore, with Neural HPLP, it is possible not only to efficiently predict if Covid-19 patients will end in severe conditions but also possible to provide an explanation of the prediction. This makes Neural HPLP explainable, interpretable, and reliable. Graphical abstract Representation of Neural HPLP. From top to bottom, the two different types of data collected from the same patient and used in this project are represented. This data feeds the two different machine learning systems and the integration of the two systems using Hierarchical Probabilistic Logic Program.

Efficient cascaded V-net optimization for lower extremity CT segmentation validated using bone morphology assessment

Semantic segmentation of bone from lower extremity computerized tomography (CT) scans can improve and accelerate the visualization, diagnosis, and surgical planning in orthopaedics. However, the large field of view of these scans makes automatic segmentation using deep learning based methods challenging, slow and graphical processing unit (GPU) memory intensive. We investigated methods to more efficiently represent anatomical context for accurate and fast segmentation and compared these with state-of-the-art methodology. Six lower extremity bones from patients of two different datasets were manually segmented from CT scans, and used to train and optimize a cascaded deep learning approach. We varied the number of resolution levels, receptive fields, patch sizes, and number of V-net blocks. The best performing network used a multi-stage, cascaded V-net approach with 128³ -64³ -32³ voxel patches as input. The average Dice coefficient over all bones was 0.98 ± 0.01, the mean surface distance was 0.26 ± 0.12 mm and the 95th percentile Hausdorff distance 0.65 ± 0.28 mm. This was a significant improvement over the results of the state-of-the-art nnU-net, with only approximately 1/12th of training time, 1/3th of inference time and 1/4th of GPU memory required. Comparison of the morphometric measurements performed on automatic and manual segmentations showed good correlation (Intraclass Correlation Coefficient [ICC] >0.8) for the alpha angle and excellent correlation (ICC >0.95) for the hip-knee-ankle angle, femoral inclination, femoral version, acetabular version, Lateral Centre-Edge angle, acetabular coverage. The segmentations were generally of sufficient quality for the tested clinical applications and were performed accurately and quickly compared to state-of-the-art methodology from the literature.

Validation of a biomechanical testing protocol of craniodorsal hip luxation in feline cadavers and comparison of two ultra-high molecular weight polyethylene materials used for extra-articular hip stabilisation

OBJECTIVES

The aim of our study was to describe a biomechanical testing protocol to reproduce ex vivo craniodorsal hip luxation specific to the feline model, and evaluate the biomechanical properties of an intact hip joint compared with the fixation strength of two different techniques of extra-articular hip stabilisation.

METHODS

Eighteen hip joints (femur and hemipelvis) were harvested from nine mature feline cadavers. CT was performed for each hip joint so that a biomechanical base specific to each joint morphotype could be created using computer-aided design. The biomechanical bases were then produced using a three-dimensional printer to secure the hip joints during testing. A total of 34 biomechanical compression tests were performed. Eighteen compression tests were performed in the control group, of which two fractured. The remaining 16 hip joints were then randomly assigned either to group A (hip joints stabilised with an extra-articular ultra-high molecular weight polyethylene (UHMWPE) implant secured by an interference screw [n = 8]) or to group B (hip joints stabilised with a UHMWPE iliofemoral suture [n = 8]).

RESULTS

Mean ± SD yield, failure load and linear stiffness in the control group were 616 ± 168 N, 666 ± 158 N and 231 ± 50 N/mm, respectively. The relative fixation strength (% of intact joint) before hip luxation in groups A and B was 43.8% and 34.7%, respectively. No statistical difference was found between groups A and B for yield and failure load. However, the reoccurrence of craniodorsal hip luxation was higher in group B than in group A, in 5/8 and 0/8 tests, respectively. Moreover, in group A, the extra-articular UHMWPE implant induced caudodorsal hip luxation, reported as failure mode in 7/8 cases.

CONCLUSIONS AND RELEVANCE

This modified biomechanical protocol for testing craniodorsal hip luxation in a feline model was validated as repeatable and with acceptable variance. The extra-articular UHMWPE implant stabilisation technique proved to be more efficient in avoiding reoccurrence of craniodorsal hip luxation than UHMWPE iliofemoral suture.

A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery

Computer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical interventions and has therefore become an increasingly popular treatment modality in maxillofacial surgery. The current maxillofacial CAS consists of three main steps: (1) CT image reconstruction, (2) bone segmentation, and (3) surgical planning. However, each of these three steps can introduce errors that can heavily affect the treatment outcome. As a consequence, tedious and time-consuming manual post-processing is often necessary to ensure that each step is performed adequately. One way to overcome this issue is by developing and implementing neural networks (NNs) within the maxillofacial CAS workflow. These learning algorithms can be trained to perform specific tasks without the need for explicitly defined rules. In recent years, an extremely large number of novel NN approaches have been proposed for a wide variety of applications, which makes it a difficult task to keep up with all relevant developments. This study therefore aimed to summarize and review all relevant NN approaches applied for CT image reconstruction, bone segmentation, and surgical planning. After full text screening, 76 publications were identified: 32 focusing on CT image reconstruction, 33 focusing on bone segmentation and 11 focusing on surgical planning. Generally, convolutional NNs were most widely used in the identified studies, although the multilayer perceptron was most commonly applied in surgical planning tasks. Moreover, the drawbacks of current approaches and promising research avenues are discussed.

Role of 18FDG PET/CT metabolic parameters in predicting hematological toxicity during chemoradiotherapy for locally advanced cervical cancer

Purpose

The aim of this study is to evaluate the value of ¹⁸FDG PET/CT metabolic parameters in predicting hematological toxicity (HT) during chemoradiotherapy (CRT) for locally advanced cervical cancer (LACC).

Methods and materials

Forty-one patients with LACC undergoing concurrent CRT were retrospectively analyzed. The correlations among age, body mass index, FIGO stage, differentiation, maximum diameter of primary lesion, parametrial invasion, lymph node metastasis, pelvic active bone marrow volume (BM_ACT), BM_ACT volume percentage (BM_ACT%), maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and HT were analyzed using hypothesis testing and logistic regression. A p-value< 0.05 was considered significant unless otherwise specified.

Results

Among the 41 patients, 19 had grade 3–4 HT and 22 had grade 0–2 HT. Only SUVmax (Z = −1.961, p = 0.050) and BM_ACT% (χ2 = 7.769, p = 0.020) showed statistically significant difference in univariate analysis. In logistic regression, grade 3–4 HT was not associated with SUVmax. The probability of HT occurrence in<30% BM_ACT% was 0.071 times less than in 30%–40% BM_ACT% (p = 0.010, OR = 0.071, 95% CI = 0.010–0.532), and the probability of HT occurrence in >40% BM_ACT% was 0.148 times less than in 30%–40% BM_ACT% (p = 0.037, OR = 0.148, 95% CI = 0.025–0.892).

Conclusion

Baseline ¹⁸FDG PET/CT BM_ACT% could help predict the severity of HT during CRT for LACC.

Image-Based Analysis of Weathered Slag for Calculation of Transport Properties and Passive Carbon Capture

Weathering of silicate-rich industrial wastes such as slag can reduce emissions from the steelmaking industry. During slag weathering, different minerals spontaneously react with atmospheric CO2 to produce calcite. Here, we evaluate the CO2 uptake during slag weathering using image-based analysis. The analysis was applied to an X-ray computed tomography (XCT) dataset of a slag sample associated with the former Ravenscraig steelworks in Lanarkshire, Scotland. The element distribution of the sample was studied using scanning electron microscopy (SEM), coupled with energy-dispersive spectroscopy (EDS). Two advanced image segmentation methods, namely trainable WEKA segmentation in the Fiji distribution of ImageJ and watershed segmentation in Avizo ® 9.3.0, were used to segment the XCT images into matrix, pore space, calcite, and other precipitates. Both methods yielded similar volume fractions of the segmented classes. However, WEKA segmentation performed better in segmenting smaller pores, while watershed segmentation was superior in overcoming the partial volume effect presented in the XCT data. We estimate that CO2 has been captured in the studied sample with an uptake between 20 and 17 kg CO2/1,000 kg slag for TWS and WS, respectively, through calcite precipitation.

Algorithms used in medical image segmentation for 3D printing and how to understand and quantify their performance

BACKGROUND

3D printing (3DP) has enabled medical professionals to create patient-specific medical devices to assist in surgical planning. Anatomical models can be generated from patient scans using a wide array of software, but there are limited studies on the geometric variance that is introduced during the digital conversion of images to models. The final accuracy of the 3D printed model is a function of manufacturing hardware quality control and the variability introduced during the multiple digital steps that convert patient scans to a printable format. This study provides a brief summary of common algorithms used for segmentation and refinement. Parameters for each that can introduce geometric variability are also identified. Several metrics for measuring variability between models and validating processes are explored and assessed.

METHODS

Using a clinical maxillofacial CT scan of a patient with a tumor of the mandible, four segmentation and refinement workflows were processed using four software packages. Differences in segmentation were calculated using several techniques including volumetric, surface, linear, global, and local measurements.

RESULTS

Visual inspection of print-ready models showed distinct differences in the thickness of the medial wall of the mandible adjacent to the tumor. Volumetric intersections and heatmaps provided useful local metrics of mismatch or variance between models made by different workflows. They also allowed calculations of aggregate percentage agreement and disagreement which provided a global benchmark metric. For the relevant regions of interest (ROIs), statistically significant differences were found in the volume and surface area comparisons for the final mandible and tumor models, as well as between measurements of the nerve central path. As with all clinical use cases, statistically significant results must be weighed against the clinical significance of any deviations found.

CONCLUSIONS

Statistically significant geometric variations from differences in segmentation and refinement algorithms can be introduced into patient-specific models. No single metric was able to capture the true accuracy of the final models. However, a combination of global and local measurements provided an understanding of important geometric variations. The clinical implications of each geometric variation is different for each anatomical location and should be evaluated on a case-by-case basis by clinicians familiar with the process. Understanding the basic segmentation and refinement functions of software is essential for sites to create a baseline from which to evaluate their standard workflows, user training, and inter-user variability when using patient-specific models for clinical interventions or decisions.

Comparison of Bone Segmentation Software over Different Anatomical Parts

Three-dimensional bone shape reconstruction is a fundamental step for any subject-specific musculo-skeletal model. Typically, medical images are processed to reconstruct bone surfaces via slice-by-slice contour identification. Freeware software packages are available, but commercial ones must be used for the necessary certification in clinics. The commercial software packages also imply expensive hardware and demanding training, but offer valuable tools. The aim of the present work is to report the performance of five commercial software packages (Mimics®, AmiraTM, D2PTM, SimplewareTM, and Segment 3D PrintTM), particularly the time to import and to create the model, the number of triangles of the mesh, and the STL file size. DICOM files of three different computed tomography scans from five different human anatomical areas were utilized for bone shape reconstruction by using each of these packages. The same operator and the same hosting hardware were used for these analyses. The computational time was found to be different between the packages analyzed, probably because of the pre-processing implied in this operation. The longer “time-to-import” observed in one software is likely due to the volume rendering during uploading. A similar number of triangles per megabyte (approximately 20 thousand) was observed for the five commercial packages. The present work showed the good performance of these software packages, with the main features being better than those analyzed previously in freeware packages.

Manufacturing Polymer Model of Anatomical Structures with Increased Accuracy Using CAx and AM Systems for Planning Orthopedic Procedures

Currently, medicine uses typical industrial structure techniques, including reverse engineering, data processing, 3D-CAD modeling, 3D printing, and coordinate measurement techniques. Taking this into account, one can notice the applications of procedures used in the aviation or automotive industries based on the structure of Industry 4.0 in the planning of operations and the production of medical models with high geometric accuracy. The procedure presented in the publication shortens the processing time of tomographic data and increases the reconstruction accuracy within the hip and knee joints. The procedure allows for the partial removal of metallic artifacts from the diagnostic image. Additionally, numerical models of anatomical structures, implants, and bone cement were developed in more detail by averaging the values of local segmentation thresholds. Before the model manufacturing process, additional tests of the PLA material were conducted in terms of its strength and thermal properties. Their goal was to select the appropriate type of PLA material for manufacturing models of anatomical structures. The numerical models were divided into parts before being manufactured using the Fused Filament Fabrication technique. The use of the modifier made it possible to change the density, type of filling, number of counters, and the type of supporting structure. These treatments allowed us to reduce costs and production time and increase the accuracy of the printout. The accuracy of the manufactured model geometry was verified using the MCA-II measuring arm with the MMDx100 laser head and surface roughness using a 3D Talyscan 150 profilometer. Using the procedure, a decrease in geometric deviations and amplitude parameters of the surface roughness were noticed. The models based on the presented approach allowed for detailed and meticulous treatment planning.

A Review and Case Study of 3D Imaging Modalities for Female Amniote Reproductive Anatomy

Recent advances in non-invasive imaging methods have revitalised the field of comparative anatomy, and reproductive anatomy has been no exception. The reproductive systems of female amniotes present specific challenges, namely their often internal "hidden" anatomy. Quantifying female reproductive systems is crucial to recognising reproductive pathologies, monitoring menstrual cycles, and understanding copulatory mechanics. Here we conduct a review of the application of non-invasive imaging techniques to female amniote reproductive anatomy. We introduce the commonly used imaging modalities of computed tomography (CT) and magnetic resonance imaging (MRI), highlighting their advantages and limitations when applied to female reproductive tissues, and make suggestions for future advances. We also include a case study of micro CT and MRI, along with their associated staining protocols, applied to cadavers of female adult stoats (Mustela erminea). In doing so, we will progress the discussion surrounding the imaging of female reproductive anatomy, whilst also impacting the fields of sexual selection research and comparative anatomy more broadly.

Additive Manufacturing in Orthopedics: A Review

Additive manufacturing is an advanced manufacturing manner that seems like the industrial revolution. It has the inborn benefit of producing complex formations, which are distinct from traditional machining technology. Its manufacturing strategy is flexible, including a wide range of materials, and its manufacturing cycle is short. Additive manufacturing techniques are progressively used in bone research and orthopedic operation as more innovative materials are developed. This Review lists the recent research results, analyzes the strengths and weaknesses of diverse three-dimensional printing strategies in orthopedics, and sums up the use of varying 3D printing strategies in surgical guides, surgical implants, surgical predictive models, and bone tissue engineering. Moreover, various postprocessing methods for additive manufacturing for orthopedics are described.

Digital planning and individual implants for secondary reconstruction of midfacial deformities: A pilot study

Objective

To evaluate the feasibility and accuracy of implementing three‐dimensional virtual surgical planning (VSP) and subsequent transfer by additive manufactured tools in the secondary reconstruction of residual post‐traumatic deformities in the midface.

Methods

Patients after secondary reconstruction of post‐traumatic midfacial deformities were included in this case series. The metrical deviation between the virtually planned and postoperative position of patient‐specific implants (PSI) and bone segments was measured at corresponding reference points. Further information collected included demographic data, post‐traumatic symptoms, and type of transfer tools.

Results

Eight consecutive patients were enrolled in the study. In five patients, VSP with subsequent manufacturing of combined predrilling/osteotomy guides and PSI was performed. In three patients, osteotomy guides, repositioning guides, and individually prebent plates were used following VSP. The median distances between the virtually planned and the postoperative position of the PSI were 2.01 mm (n = 18) compared to a median distance concerning the bone segments of 3.05 mm (n = 12). In patients where PSI were used, the median displacement of the bone segments was lower (n = 7, median 2.77 mm) than in the group with prebent plates (n = 5, 3.28 mm).

Conclusion

This study demonstrated the feasibility of VSP and transfer by additive manufactured tools for the secondary reconstruction of complex residual post‐traumatic deformities in the midface. However, the median deviations observed in this case series were unexpectedly high. The use of navigational systems may further improve the level of accuracy.

To evaluate the feasibility and accuracy of implementing three‐dimensional virtual surgical planning (VSP) and subsequent transfer by additive manufactured tools in the secondary reconstruction of residual post‐traumatic deformities in the midface. This study demonstrated the feasibility of VSP and transfer by additive manufactured tools for the secondary reconstruction of complex residual post‐traumatic deformities in the midface. However, the median deviations observed in this case series were unexpectedly high. The use of navigational systems may further improve the level of accuracy.

Validation and accuracy evaluation of automatic segmentation for knee joint pre-planning

BACKGROUND

Proper use of three-dimensional (3D) models generated from medical imaging data in clinical preoperative planning, training and consultation is based on the preliminary proved accuracy of the replication of the patient anatomy. Therefore, this study investigated the dimensional accuracy of 3D reconstructions of the knee joint generated from computed tomography scans via automatic segmentation by comparing them with 3D models generated through manual segmentation.

METHODS

Three unpaired, fresh-frozen right legs were investigated. Three-dimensional models of the femur and the tibia of each leg were manually segmented using a commercial software and compared in terms of geometrical accuracy with the 3D models automatically segmented using proprietary software. Bony landmarks were identified and used to calculate clinically relevant distances: femoral epicondylar distance; posterior femoral epicondylar distance; femoral trochlear groove length; tibial knee center tubercle distance (TKCTD). Pearson's correlation coefficient and Bland and Altman plots were used to evaluate the level of agreement between measured distances.

RESULTS

Differences between parameters measured on 3D models manually and automatically segmented were below 1 mm (range: -0.06 to 0.72 mm), except for TKCTD (between 1.00 and 1.40 mm in two specimens). In addition, there was a significant strong correlation between measurements.

CONCLUSIONS

The results obtained are comparable to those reported in previous studies where accuracy of bone 3D reconstruction was investigated. Automatic segmentation techniques can be used to quickly reconstruct reliable 3D models of bone anatomy and these results may contribute to enhance the spread of this technology in preoperative and operative settings, where it has shown considerable potential.

In-House, Open-Source 3D-Software-Based, CAD/CAM-Planned Mandibular Reconstructions in 20 Consecutive Free Fibula Flap Cases: An Explorative Cross-Sectional Study With Three-Dimensional Performance Analysis

Background

Mandibular reconstruction is conventionally performed freehand, CAD/CAM-assisted, or by using partially adjustable resection aids. CAD/CAM-assisted reconstructions are usually done in cooperation with osteosynthesis manufacturers, which entails additional costs and longer lead time. The purpose of this study is to analyze an in-house, open-source software-based solution for virtual planning.

Methods and Materials

All consecutive cases between January 2019 and April 2021 that underwent in-house, software-based (Blender) mandibular reconstruction with a free fibula flap (FFF) were included in this cross-sectional study. The pre- and postoperative Digital Imaging and Com munications in Medicine (DICOM) data were converted to standard tessellation language (STL) files. In addition to documenting general information (sex, age, indication for surgery, extent of resection, number of segments, duration of surgery, and ischemia time), conventional measurements and three-dimensional analysis methods (root mean square error [RMSE], mean surface distance [MSD], and Hausdorff distance [HD]) were used.

Results

Twenty consecutive cases were enrolled. Three-dimensional analysis of preoperative and virtually planned neomandibula models was associated with a median RMSE of 1.4 (0.4–7.2), MSD of 0.3 (-0.1–2.9), and HD of 0.7 (0.1–3.1). Three-dimensional comparison of preoperative and postoperative models showed a median RMSE of 2.2 (1.5–11.1), MSD of 0.5 (-0.6–6.1), and HD of 1.5 (1.1–6.5) and the differences were significantly different for RMSE (p < 0.001) and HD (p < 0.001). The difference was not significantly different for MSD (p = 0.554). Three-dimensional analysis of virtual and postoperative models had a median RMSE of 2.3 (1.3–10.7), MSD of -0.1 (-1.0–5.6), and HD of 1.7 (0.1–5.9).

Conclusions

Open-source software-based in-house planning is a feasible, inexpensive, and fast method that enables accurate reconstructions. Additionally, it is excellent for teaching purposes.

Accuracy of free-hand positioned patient specific implants (PSI) in primary reconstruction after inferior and/or medial orbital wall fractures

BACKGROUND

To assess the accuracy with which CAD/CAM-fabricated patient-specific titanium implants (PSI) are positioned for inferior and/or medial orbital wall reconstruction without the use of intraoperative navigation.

METHODS

Patients who underwent a primary reconstruction of the orbital walls with PSI due to fractures were enrolled in this retrospective cohort analysis. The primary outcome variables were the mean surface distances (MSD) between virtually planned and postoperative PSI position and single linear deviations in the x-, y- and z-axis at corresponding reference points. Secondary outcome variables included demographic data, classification of orbital wall defects and clinical outcomes.

RESULTS

A total of 33 PSI (orbital floor n = 22; medial wall, n = 11) were examined in 27 patients. MSD was on a comparable level for the orbital floor and medial wall (median 0.39 mm, range 0.22-1.53 mm vs. median 0.42 mm, range 0.21-0.98 mm; p = 0.56). Single linear deviations were lower for reconstructions of the orbital floor compared to the medial wall (median 0.45 vs. 0.79 mm; p < 0.05). There was no association between the occurrence of diplopia and the accuracy level (p = 0.418).

CONCLUSIONS

Free-hand positioning of PSI reaches a clinically appropriate level of accuracy, limiting the necessity of navigational systems to selected cases.

Angular and linear measurements of adult flexible flatfoot via weight-bearing CT scans and 3D bone reconstruction tools

Acquired adult flatfoot is a frequent deformity which implies multiple, complex and combined 3D modifications of the foot skeletal structure. The difficult thorough evaluation of the degree of severity pre-op and the corresponding assessment post-op can now be overcome by cone-beam (CBCT) technology, which can provide access to the 3D skeletal structure in weight-bearing. This study aims to report flatfoot deformities originally in 3D and in weight-bearing, with measurements taken using two different bone segmentation techniques. 21 such patients, with indication for surgical corrections, underwent CBCT (Carestream, US) while standing on one leg. From these scans, 3D models of each bone of the foot were reconstructed by using two different state-of-the-art segmentation tools: a semi-automatic (Mimics Innovation Suite, Materialise, Belgium), and an automatic (Bonelogic Ortho Foot and Ankle, Disior, Finland). From both reconstructed models, Principal Component Analysis was used to define anatomical reference frames, and original foot and ankle angles and other parameters were calculated mostly based on the longitudinal axis of the bones, in anatomical plane projections and in 3D. Both bone model reconstructions revealed a considerable valgus of the calcareous, plantarflexion and internal rotation of the talus, and typical Meary’s angles in the lateral and transverse plane projections. The mean difference from these angles between semi-automatic and automatic segmentations was larger than 3.5 degrees for only 3 of the 32 measurements, and a large number of these differences were not statistically significant. CBCT and the present techniques for bone shape reconstruction finally provide a novel and valuable 3D assessment of complex foot deformities in weight-bearing, eliminating previous limitations associated to unloaded feet and bidimensional measures. Corresponding measurements on the bone models from the two segmentation tools compared well. Other more representative measurements can be defined in the future using CBCT and these techniques.

Geometric accuracy of magnetic resonance imaging-derived virtual 3-dimensional bone surface models of the mandible in comparison to computed tomography and cone beam computed tomography: A porcine cadaver study

BACKGROUND

Providing accurate 3-dimensional virtual bone surface models is a prerequisite for virtual surgical planning and additive manufacturing in craniomaxillofacial surgery. For this purpose, magnetic resonance imaging (MRI) may be a radiation-free alternative to computed tomography (CT) and cone beam computed tomography (CBCT).

PURPOSE

The aim of this study was to assess the geometric accuracy of 3-dimensional T1-weighted MRI-derived virtual bone surface models of the mandible in comparison to CT and CBCT.

MATERIALS AND METHODS

Specimens of the mandible from porcine cadavers were scanned with (1) a 3-dimensional T1-weighted MRI sequence (0.6 mm isotropic voxel) optimized for bone imaging, (2) CT, and (3) CBCT. Cortical mandibular structures (n = 10) were segmented using semiautomated and manual techniques. Imaging-based virtual 3-dimensional models were aligned with a high-resolution optical 3-dimensional surface scan of the dissected bone (=ground truth) and global geometric deviations were calculated (mean surface distance [MSD]/root-mean-square distance [RMSD]). Agreement between the imaging modalities was assessed by equivalence testing and Bland-Altman analysis.

RESULTS

Intra- and inter-rater agreement was on a high level for all modalities. Global geometric deviations (MSD/RMSD) between optical scans and imaging modalities were 0.225 ± 0.020 mm/0.345 ± 0.074 mm for CT, 0.280 ± 0.067 mm/0.371 ± 0.074 mm for MRI, and 0.352 ± 0.076 mm/0.454 ± 0.071 mm for CBCT. All imaging modalities were statistically equivalent within an equivalence margin of ±0.3 mm, and Bland-Altman analysis indicated high agreement as well.

CONCLUSIONS

The results of this study indicate that the accuracy and reliability of MRI-derived virtual 3-dimensional bone surface models is equal to CT and CBCT. MRI may be considered as a reliable alternative to CT and CBCT in computer-assisted craniomaxillofacial surgery.

Estimating the Accuracy of Mandible Anatomical Models Manufactured Using Material Extrusion Methods

The development of new solutions in craniofacial surgery brings the need to increase the accuracy of 3D printing models. The accuracy of the manufactured models is most often verified using optical coordinate measuring systems. However, so far, no decision has been taken regarding which type of system would allow for a reliable estimation of the geometrical accuracy of the anatomical models. Three types of optical measurement systems (Atos III Triple Scan, articulated arm (MCA-II) with a laser head (MMD × 100), and Benchtop CT160Xi) were used to verify the accuracy of 12 polymer anatomical models of the left side of the mandible. The models were manufactured using fused deposition modeling (FDM), melted and extruded modeling (MEM), and fused filament fabrication (FFF) techniques. The obtained results indicate that the Atos III Triple Scan allows for the most accurate estimation of errors in model manufacturing. Using the FDM technique obtained the best accuracy in models manufactured (0.008 ± 0.118 mm for ABS0-M30 and 0.016 ± 0.178 mm for PC-10 material). A very similar value of the standard deviation of PLA and PET material was observed (about 0.180 mm). The worst results were observed in the MEM technique (0.012 mm ± 0.308 mm). The knowledge regarding the precisely evaluated errors in manufactured models within the mandibular area will help in the controlled preparation of templates regarding the expected accuracy of surgical operations.