Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review

Image-based Mandibular and Maxillary Parcellation and Annotation using Computer Tomography (IMPACT): A Deep Learning-based Clinical Tool for Orodental Dose Estimation and Osteoradionecrosis Assessment

Background

Accurate delineation of orodental structures on radiotherapy CT images is essential for dosimetric assessments and dental decisions. We propose a deep-learning auto-segmentation framework for individual teeth and mandible/maxilla sub-volumes aligned with the ClinRad ORN staging system.

Methods

Mandible and maxilla sub-volumes were manually defined, differentiating between alveolar and basal regions, and teeth were labelled individually. For each task, a DL segmentation model was independently trained. A Swin UNETR-based model was used for the mandible sub-volumes. For the smaller structures (e.g., teeth and maxilla sub-volumes) a two-stage segmentation model first used the ResUNet to segment the entire teeth and maxilla regions as a single ROI that was then used to crop the image input of the Swin UNETR. In addition to segmentation accuracy and geometric precision, a dosimetric comparison was made between manual and model-predicted segmentations.

Results

Segmentation performance varied across sub-volumes - mean Dice values of 0.85 (mandible basal), 0.82 (mandible alveolar), 0.78 (maxilla alveolar), 0.80 (upper central teeth), 0.69 (upper premolars), 0.76 (upper molars), 0.76 (lower central teeth), 0.70 (lower premolars), 0.71 (lower molars) - and exhibited limited applicability in segmenting teeth and sub-volumes often absent in the data. Only the maxilla alveolar central sub-volume showed a statistically significant dosimetric difference (Bonferroni-adjusted p-value = 0.02).

Conclusion

We present a novel DL-based auto-segmentation framework of orodental structures, enabling spatial localization of dose-related differences in the jaw. This tool enhances image-based bone injury detection, including ORN, and improves clinical decision-making in radiation oncology and dental care for head and neck cancer patients.

Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review

OBJECTIVE

The aim of this review was to evaluate the accuracy of artificial intelligence (AI) in the segmentation of teeth, jawbone (maxilla, mandible with temporomandibular joint), and mandibular (inferior alveolar) canal in CBCT and CT scans.

MATERIALS AND METHODS

Articles were retrieved from MEDLINE, Cochrane CENTRAL, IEEE Xplore, and Google Scholar. Eligible studies were analyzed thematically, and their quality was appraised using the JBI checklist for diagnostic test accuracy studies. Meta-analysis was conducted for key performance metrics, including Dice Similarity Coefficient (DSC) and Average Surface Distance (ASD).

RESULTS

A total of 767 non-duplicate articles were identified, and 30 studies were included in the review. Of these, 27 employed deep-learning models, while 3 utilized classical machine-learning approaches. The pooled DSC for mandible segmentation was 0.94 (95% CI: 0.91-0.98), mandibular canal segmentation was 0.694 (95% CI: 0.551-0.838), maxilla segmentation was 0.907 (95% CI: 0.867-0.948), and teeth segmentation was 0.925 (95% CI: 0.891-0.959). Pooled ASD values were 0.534 mm (95% CI: 0.366-0.703) for the mandibular canal, 0.468 mm (95% CI: 0.295-0.641) for the maxilla, and 0.189 mm (95% CI: 0.043-0.335) for teeth. Other metrics, such as sensitivity and precision, were variably reported, with sensitivity exceeding 90% across studies.

CONCLUSION

AI-based segmentation, particularly using deep-learning models, demonstrates high accuracy in the segmentation of dental and maxillofacial structures, comparable to expert manual segmentation. The integration of AI into clinical workflows offers not only accuracy but also substantial time savings, positioning it as a promising tool for automated dental imaging.

Assessment of deep learning technique for fully automated mandibular segmentation

INTRODUCTION

This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.

METHODS

A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model. To assess the model's performance, 15 cone-beam computed tomography scans from the test group were inputted into the model. The ground truth was obtained from manual segmentation data. Metrics including the Dice similarity coefficient, Hausdorff 95%, precision, recall, and segmentation times were calculated. In addition, surface deviations and volumetric differences between the automated and manual segmentation results were analyzed.

RESULTS

The automated model showed a high level of similarity to the manual segmentation results, with a mean Dice similarity coefficient of 0.926 ± 0.014. The Hausdorff distance was 1.358 ± 0.466 mm, whereas the mean recall and precision values were 0.941 ± 0.028 and 0.941 ± 0.022, respectively. There were no statistically significant differences in the arithmetic mean of the surface deviation for the entire mandible and 11 different anatomic regions. In terms of volumetric comparisons, the difference between the 2 groups was 1.62 mm³, which was not statistically significant.

CONCLUSIONS

The automated model was found to be suitable for clinical use, demonstrating a high degree of agreement with the reference manual method. Clinicians can use open-source software to develop custom automated segmentation models tailored to their specific needs.

Automated tooth segmentation in magnetic resonance scans using deep learning - A pilot study

OBJECTIVES

The main objective was to develop and evaluate an artificial intelligence model for tooth segmentation in magnetic resonance (MR) scans.

METHODS

MR scans of 20 patients performed with a commercial 64-channel head coil with a T1-weighted 3D-SPACE (Sampling Perfection with Application Optimized Contrasts using different flip angle Evolution) sequence were included. Sixteen datasets were used for model training and 4 for accuracy evaluation. Two clinicians segmented and annotated the teeth in each dataset. A segmentation model was trained using the nnU-Net framework. The manual reference tooth segmentation and the inferred tooth segmentation were superimposed and compared by computing precision, sensitivity, and Dice-Sørensen coefficient. Surface meshes were extracted from the segmentations, and the distances between points on each mesh and their closest counterparts on the other mesh were computed, of which the mean (average symmetric surface distance) and 95th percentile (Hausdorff distance 95%, HD95) were reported.

RESULTS

The model achieved an overall precision of 0.867, a sensitivity of 0.926, a Dice-Sørensen coefficient of 0.895, and a 95% Hausdorff distance of 0.91 mm. The model predictions were less accurate for datasets containing dental restorations due to image artefacts.

CONCLUSIONS

The current study developed an automated method for tooth segmentation in MR scans with moderate to high effectiveness for scans with respectively without artefacts.

Deep learning segmentation of mandible with lower dentition from cone beam CT

OBJECTIVES

This study aimed to train a 3D U-Net convolutional neural network (CNN) for mandible and lower dentition segmentation from cone-beam computed tomography (CBCT) scans.

METHODS

In an ambispective cross-sectional design, CBCT scans from two hospitals (2009-2019 and 2021-2022) constituted an internal dataset and external validation set, respectively. Manual segmentation informed CNN training, and evaluations employed Dice similarity coefficient (DSC) for volumetric accuracy. A blinded oral maxillofacial surgeon performed qualitative grading of CBCT scans and object meshes. Statistical analyses included independent t-tests and ANOVA tests to compare DSC across patient subgroups of gender, race, body mass index (BMI), test dataset used, age, and degree of metal artifact. Tests were powered for a minimum detectable difference in DSC of 0.025, with alpha of 0.05 and power level of 0.8.

RESULTS

648 CBCT scans from 490 patients were included in the study. The CNN achieved high accuracy (average DSC: 0.945 internal, 0.940 external). No DSC differences were observed between test set used, gender, BMI, and race. Significant differences in DSC were identified based on age group and the degree of metal artifact. The majority (80%) of object meshes produced by both manual and automatic segmentation were rated as acceptable or higher quality.

CONCLUSION

We developed a model for automatic mandible and lower dentition segmentation from CBCT scans in a demographically diverse cohort including a high degree of metal artifacts. The model demonstrated good accuracy on internal and external test sets, with majority acceptable quality from a clinical grader.

Automatic classification and segmentation of multiclass jaw lesions in cone-beam CT using deep learning

OBJECTIVES

To develop and validate a modified deep learning (DL) model based on nnU-Net for classifying and segmenting five-class jaw lesions using cone-beam CT (CBCT).

METHODS

A total of 368 CBCT scans (37 168 slices) were used to train a multi-class segmentation model. The data underwent manual annotation by two oral and maxillofacial surgeons (OMSs) to serve as ground truth. Sensitivity, specificity, precision, F1-score, and accuracy were used to evaluate the classification ability of the model and doctors, with or without artificial intelligence assistance. The dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and segmentation time were used to evaluate the segmentation effect of the model.

RESULTS

The model achieved the dual task of classifying and segmenting jaw lesions in CBCT. For classification, the sensitivity, specificity, precision, and accuracy of the model were 0.871, 0.974, 0.874, and 0.891, respectively, surpassing oral and maxillofacial radiologists (OMFRs) and OMSs, approaching the specialist. With the model's assistance, the classification performance of OMFRs and OMSs improved, particularly for odontogenic keratocyst (OKC) and ameloblastoma (AM), with F1-score improvements ranging from 6.2% to 12.7%. For segmentation, the DSC was 87.2% and the ASSD was 1.359 mm. The model's average segmentation time was 40 ± 9.9 s, contrasting with 25 ± 7.2 min for OMSs.

CONCLUSIONS

The proposed DL model accurately and efficiently classified and segmented five classes of jaw lesions using CBCT. In addition, it could assist doctors in improving classification accuracy and segmentation efficiency, particularly in distinguishing confusing lesions (eg, AM and OKC).

Design and evaluation of a deep learning-based automatic segmentation of maxillary and mandibular substructures using a 3D U-Net

Background

Current segmentation approaches for radiation treatment planning in head and neck cancer patients (HNCP) typically consider the entire mandible as an organ at risk, whereas segmentation of the maxilla remains uncommon. Accurate risk assessment for osteoradionecrosis (ORN) or implant-based dental rehabilitation after radiation therapy may require a nuanced analysis of dose distribution in specific mandibular and maxillary segments. Manual segmentation is time-consuming and inconsistent, and there is no definition of jaw subsections.

Materials and methods

The mandible and maxilla were divided into 12 substructures. The model was developed from 82 computed tomography (CT) scans of HNCP and adopts an encoder-decoder three-dimensional (3D) U-Net structure. The efficiency and accuracy of the automated method were compared against manual segmentation on an additional set of 20 independent CT scans. The evaluation metrics used were the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and surface DSC (sDSC).

Results

Automated segmentations were performed in a median of 86 s, compared to manual segmentations, which took a median of 53.5 min. The median DSC per substructure ranged from 0.81 to 0.91, and the median HD95 ranged from 1.61 to 4.22. The number of artifacts did not affect these scores. The maxillary substructures showed lower metrics than the mandibular substructures.

Conclusions

The jaw substructure segmentation demonstrated high accuracy, time efficiency, and promising results in CT scans with and without metal artifacts. This novel model could provide further investigation into dose relationships with ORN or dental implant failure in normal tissue complication prediction models.

Evaluating tooth segmentation accuracy and time efficiency in CBCT images using artificial intelligence: A systematic review and Meta-analysis

OBJECTIVES

This systematic review and meta-analysis aimed to assess the current performance of artificial intelligence (AI)-based methods for tooth segmentation in three-dimensional cone-beam computed tomography (CBCT) images, with a focus on their accuracy and efficiency compared to those of manual segmentation techniques.

DATA

The data analyzed in this review consisted of a wide range of research studies utilizing AI algorithms for tooth segmentation in CBCT images. Meta-analysis was performed, focusing on the evaluation of the segmentation results using the dice similarity coefficient (DSC).

SOURCES

PubMed, Embase, Scopus, Web of Science, and IEEE Explore were comprehensively searched to identify relevant studies. The initial search yielded 5642 entries, and subsequent screening and selection processes led to the inclusion of 35 studies in the systematic review. Among the various segmentation methods employed, convolutional neural networks, particularly the U-net model, are the most commonly utilized. The pooled effect of the DSC score for tooth segmentation was 0.95 (95 %CI 0.94 to 0.96). Furthermore, seven papers provided insights into the time required for segmentation, which ranged from 1.5 s to 3.4 min when utilizing AI techniques.

CONCLUSIONS

AI models demonstrated favorable accuracy in automatically segmenting teeth from CBCT images while reducing the time required for the process. Nevertheless, correction methods for metal artifacts and tooth structure segmentation using different imaging modalities should be addressed in future studies.

CLINICAL SIGNIFICANCE

AI algorithms have great potential for precise tooth measurements, orthodontic treatment planning, dental implant placement, and other dental procedures that require accurate tooth delineation. These advances have contributed to improved clinical outcomes and patient care in dental practice.

Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning including unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemmas in multi-organ segmentation. We first review the fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends.

Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images

OBJECTIVES

The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.

METHODS

In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.

RESULTS

F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.

CONCLUSIONS

Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.

Semi or fully automatic tooth segmentation in CBCT images: a review

Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions.

Autologous Transplantation Tooth Guide Design Based on Deep Learning

BACKGROUND

Autologous tooth transplantation requires precise surgical guide design, involving manual tracing of donor tooth contours based on patient cone-beam computed tomography (CBCT) scans. While manual corrections are time-consuming and prone to human errors, deep learning-based approaches show promise in reducing labor and time costs while minimizing errors. However, the application of deep learning techniques in this particular field is yet to be investigated.

PURPOSE

We aimed to assess the feasibility of replacing the traditional design pipeline with a deep learning-enabled autologous tooth transplantation guide design pipeline.

STUDY DESIGN, SETTING, SAMPLE

This retrospective cross-sectional study used 79 CBCT images collected at the Guangzhou Medical University Hospital between October 2022 and March 2023. Following preprocessing, a total of 5,070 region of interest images were extracted from 79 CBCT images.

PREDICTOR VARIABLE

Autologous tooth transplantation guide design pipelines, either based on traditional manual design or deep learning-based design.

MAIN OUTCOME VARIABLE

The main outcome variable was the error between the reconstructed model and the gold standard benchmark. We used the third molar extracted clinically as the gold standard and leveraged it as the benchmark for evaluating our reconstructed models from different design pipelines. Both trueness and accuracy were used to evaluate this error. Trueness was assessed using the root mean square (RMS), and accuracy was measured using the standard deviation. The secondary outcome variable was the pipeline efficiency, assessed based on the time cost. Time cost refers to the amount of time required to acquire the third molar model using the pipeline.

ANALYSES

Data were analyzed using the Kruskal-Wallis test. Statistical significance was set at P < .05.

RESULTS

In the surface matching comparison for different reconstructed models, the deep learning group achieved the lowest RMS value (0.335 ± 0.066 mm). There were no significant differences in RMS values between manual design by a senior doctor and deep learning-based design (P = .688), and the standard deviation values did not differ among the 3 groups (P = .103). The deep learning-based design pipeline (0.017 ± 0.001 minutes) provided a faster assessment compared to the manual design pipeline by both senior (19.676 ± 2.386 minutes) and junior doctors (30.613 ± 6.571 minutes) (P < .001).

CONCLUSIONS AND RELEVANCE

The deep learning-based automatic pipeline exhibited similar performance in surgical guide design for autogenous tooth transplantation compared to manual design by senior doctors, and it minimized time costs.

Advancements in oral and maxillofacial surgery medical images segmentation techniques: An overview

OBJECTIVES

This article reviews recent advances in computer-aided segmentation methods for oral and maxillofacial surgery and describes the advantages and limitations of these methods. The objective is to provide an invaluable resource for precise therapy and surgical planning in oral and maxillofacial surgery. Study selection, data and sources: This review includes full-text articles and conference proceedings reporting the application of segmentation methods in the field of oral and maxillofacial surgery. The research focuses on three aspects: tooth detection segmentation, mandibular canal segmentation and alveolar bone segmentation. The most commonly used imaging technique is CBCT, followed by conventional CT and Orthopantomography. A systematic electronic database search was performed up to July 2023 (Medline via PubMed, IEEE Xplore, ArXiv, Google Scholar were searched).

RESULTS

These segmentation methods can be mainly divided into two categories: traditional image processing and machine learning (including deep learning). Performance testing on a dataset of images labeled by medical professionals shows that it performs similarly to dentists' annotations, confirming its effectiveness. However, no studies have evaluated its practical application value.

CONCLUSION

Segmentation methods (particularly deep learning methods) have demonstrated unprecedented performance, while inherent challenges remain, including the scarcity and inconsistency of datasets, visible artifacts in images, unbalanced data distribution, and the "black box" nature.

CLINICAL SIGNIFICANCE

Accurate image segmentation is critical for precise treatment and surgical planning in oral and maxillofacial surgery. This review aims to facilitate more accurate and effective surgical treatment planning among dental researchers.

Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review

OBJECTIVE

The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination.

OVERVIEW

An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed.

CONCLUSIONS

Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice.

CLINICAL SIGNIFICANCE

AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.

Deep-learning-based automatic facial bone segmentation using a two-dimensional U-Net

The use of deep learning (DL) in medical imaging is becoming increasingly widespread. Although DL has been used previously for the segmentation of facial bones in computed tomography (CT) images, there are few reports of segmentation involving multiple areas. In this study, a U-Net was used to investigate the automatic segmentation of facial bones into eight areas, with the aim of facilitating virtual surgical planning (VSP) and computer-aided design and manufacturing (CAD/CAM) in maxillofacial surgery. CT data from 50 patients were prepared and used for training, and five-fold cross-validation was performed. The output results generated by the DL model were validated by Dice coefficient and average symmetric surface distance (ASSD). The automatic segmentation was successful in all cases, with a mean± standard deviation Dice coefficient of 0.897 ± 0.077 and ASSD of 1.168 ± 1.962 mm. The accuracy was very high for the mandible (Dice coefficient 0.984, ASSD 0.324 mm) and zygomatic bones (Dice coefficient 0.931, ASSD 0.487 mm), and these could be introduced for VSP and CAD/CAM without any modification. The results for other areas, particularly the teeth, were slightly inferior, with possible reasons being the effects of defects, bonded maxillary and mandibular teeth, and metal artefacts. A limitation of this study is that the data were from a single institution. Hence further research is required to improve the accuracy for some facial areas and to validate the results in larger and more diverse populations.

Tooth automatic segmentation from CBCT images: a systematic review

OBJECTIVES

To describe the current state of the art regarding technological advances in full-automatic tooth segmentation approaches from 3D cone-beam computed tomography (CBCT) images.

MATERIALS AND METHODS

In March 2023, a search strategy without a timeline setting was carried out through a combination of MeSH terms and free text words pooled through Boolean operators ('AND', 'OR') on the following databases: PubMed, Scopus, Web of Science and IEEE Explore. Randomized and non-randomized controlled trials, cohort, case-control, cross-sectional and retrospective studies in the English language only were included.

RESULTS

The search strategy identified 541 articles, of which 23 have been selected. The most employed segmentation methods were based on deep learning approaches. One article exposed an automatic approach for tooth segmentation based on a watershed algorithm and another article used an improved level set method. Four studies presented classical machine learning and thresholding approaches. The most employed metric for evaluating segmentation performance was the Dice similarity index which ranged from 90 ± 3% to 97.9 ± 1.5%.

CONCLUSIONS

Thresholding appeared not reliable for tooth segmentation from CBCT images, whereas convolutional neural networks (CNNs) have been demonstrated as the most promising approach. CNNs could help overcome tooth segmentation's main limitations from CBCT images related to root anatomy, heavy scattering, immature teeth, metal artifacts and time consumption. New studies with uniform protocols and evaluation metrics with random sampling and blinding for data analysis are encouraged to objectively compare the different deep learning architectures' reliability.

CLINICAL RELEVANCE

Automatic tooth segmentation's best performance has been obtained through CNNs for the different ambits of digital dentistry.

Improved Repeatability of Mouse Tibia Volume Segmentation in Murine Myelofibrosis Model Using Deep Learning

A murine model of myelofibrosis in tibia was used in a co-clinical trial to evaluate segmentation methods for application of image-based biomarkers to assess disease status. The dataset (32 mice with 157 3D MRI scans including 49 test-retest pairs scanned on consecutive days) was split into approximately 70% training, 10% validation, and 20% test subsets. Two expert annotators (EA1 and EA2) performed manual segmentations of the mouse tibia (EA1: all data; EA2: test and validation). Attention U-net (A-U-net) model performance was assessed for accuracy with respect to EA1 reference using the average Jaccard index (AJI), volume intersection ratio (AVI), volume error (AVE), and Hausdorff distance (AHD) for four training scenarios: full training, two half-splits, and a single-mouse subsets. The repeatability of computer versus expert segmentations for tibia volume of test-retest pairs was assessed by within-subject coefficient of variance (%wCV). A-U-net models trained on full and half-split training sets achieved similar average accuracy (with respect to EA1 annotations) for test set: AJI = 83-84%, AVI = 89-90%, AVE = 2-3%, and AHD = 0.5 mm-0.7 mm, exceeding EA2 accuracy: AJ = 81%, AVI = 83%, AVE = 14%, and AHD = 0.3 mm. The A-U-net model repeatability wCV [95% CI]: 3 [2, 5]% was notably better than that of expert annotators EA1: 5 [4, 9]% and EA2: 8 [6, 13]%. The developed deep learning model effectively automates murine bone marrow segmentation with accuracy comparable to human annotators and substantially improved repeatability.

Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network

PURPOSE

For computer-aided planning of facial bony surgery, the creation of high-resolution 3D-models of the bones by segmenting volume imaging data is a labor-intensive step, especially as metal dental inlays or implants cause severe artifacts that reduce the quality of the computer-tomographic imaging data. This study provides a method to segment accurate, artifact-free 3D surface models of mandibles from CT data using convolutional neural networks.

METHODS

The presented approach cascades two independently trained 3D-U-Nets to perform accurate segmentations of the mandible bone from full resolution CT images. The networks are trained in different settings using three different loss functions and a data augmentation pipeline. Training and evaluation datasets consist of manually segmented CT images from 307 dentate and edentulous individuals, partly with heavy imaging artifacts. The accuracy of the models is measured using overlap-based, surface-based and anatomical-curvature-based metrics.

RESULTS

Our approach produces high-resolution segmentations of the mandibles, coping with severe imaging artifacts in the CT imaging data. The use of the two-stepped approach yields highly significant improvements to the prediction accuracies. The best models achieve a Dice coefficient of 94.824% and an average surface distance of 0.31 mm on our test dataset.

CONCLUSION

The use of two cascaded U-Net allows high-resolution predictions for small regions of interest in the imaging data. The proposed method is fast and allows a user-independent image segmentation, producing objective and repeatable results that can be used in automated surgical planning procedures.

Three-Dimensional Innovations in Personalized Surgery

Due to the introduction of three-dimensional (3D) technology in surgery, it has become possible to preoperatively plan complex bone resections and reconstructions, (corrections and adjustments related to bones), from head to toe [...].

Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review

This study aimed at performing a systematic review of the literature on the application of artificial intelligence (AI) in dental and maxillofacial cone beam computed tomography (CBCT) and providing comprehensive descriptions of current technical innovations to assist future researchers and dental professionals. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) Statement was followed. The study's protocol was prospectively registered. Following databases were searched, based on MeSH and Emtree terms: PubMed/MEDLINE, Embase and Web of Science. The search strategy enrolled 1473 articles. 59 publications were included, which assessed the use of AI on CBCT images in dentistry. According to the PROBAST guidelines for study design, seven papers reported only external validation and 11 reported both model building and validation on an external dataset. 40 studies focused exclusively on model development. The AI models employed mainly used deep learning models (42 studies), while other 17 papers used conventional approaches, such as statistical-shape and active shape models, and traditional machine learning methods, such as thresholding-based methods, support vector machines, k-nearest neighbors, decision trees, and random forests. Supervised or semi-supervised learning was utilized in the majority (96.62%) of studies, and unsupervised learning was used in two (3.38%). 52 publications included studies had a high risk of bias (ROB), two papers had a low ROB, and four papers had an unclear rating. Applications based on AI have the potential to improve oral healthcare quality, promote personalized, predictive, preventative, and participatory dentistry, and expedite dental procedures.

Establishing a Point-of-Care Virtual Planning and 3D Printing Program

Virtual surgical planning (VSP) and three-dimensional (3D) printing have become a standard of care at our institution, transforming the surgical care of complex patients. Patient-specific, anatomic models and surgical guides are clinically used to improve multidisciplinary communication, presurgical planning, intraoperative guidance, and the patient informed consent. Recent innovations have allowed both VSP and 3D printing to become more accessible to various sized hospital systems. Insourcing such work has several advantages including quicker turnaround times and increased innovation through collaborative multidisciplinary teams. Centralizing 3D printing programs at the point-of-care provides a greater cost-efficient investment for institutions. The following article will detail capital equipment needs, institutional structure, operational personnel, and other considerations necessary in the establishment of a POC manufacturing program.

Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review

Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.

Videomics of the Upper Aero-Digestive Tract Cancer: Deep Learning Applied to White Light and Narrow Band Imaging for Automatic Segmentation of Endoscopic Images

Introduction

Narrow Band Imaging (NBI) is an endoscopic visualization technique useful for upper aero-digestive tract (UADT) cancer detection and margins evaluation. However, NBI analysis is strongly operator-dependent and requires high expertise, thus limiting its wider implementation. Recently, artificial intelligence (AI) has demonstrated potential for applications in UADT videoendoscopy. Among AI methods, deep learning algorithms, and especially convolutional neural networks (CNNs), are particularly suitable for delineating cancers on videoendoscopy. This study is aimed to develop a CNN for automatic semantic segmentation of UADT cancer on endoscopic images.

Materials and Methods

A dataset of white light and NBI videoframes of laryngeal squamous cell carcinoma (LSCC) was collected and manually annotated. A novel DL segmentation model (SegMENT) was designed. SegMENT relies on DeepLabV3+ CNN architecture, modified using Xception as a backbone and incorporating ensemble features from other CNNs. The performance of SegMENT was compared to state-of-the-art CNNs (UNet, ResUNet, and DeepLabv3). SegMENT was then validated on two external datasets of NBI images of oropharyngeal (OPSCC) and oral cavity SCC (OSCC) obtained from a previously published study. The impact of in-domain transfer learning through an ensemble technique was evaluated on the external datasets.

Results

219 LSCC patients were retrospectively included in the study. A total of 683 videoframes composed the LSCC dataset, while the external validation cohorts of OPSCC and OCSCC contained 116 and 102 images. On the LSCC dataset, SegMENT outperformed the other DL models, obtaining the following median values: 0.68 intersection over union (IoU), 0.81 dice similarity coefficient (DSC), 0.95 recall, 0.78 precision, 0.97 accuracy. For the OCSCC and OPSCC datasets, results were superior compared to previously published data: the median performance metrics were, respectively, improved as follows: DSC=10.3% and 11.9%, recall=15.0% and 5.1%, precision=17.0% and 14.7%, accuracy=4.1% and 10.3%.

Conclusion

SegMENT achieved promising performances, showing that automatic tumor segmentation in endoscopic images is feasible even within the highly heterogeneous and complex UADT environment. SegMENT outperformed the previously published results on the external validation cohorts. The model demonstrated potential for improved detection of early tumors, more precise biopsies, and better selection of resection margins.

Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values

With the development of big data and cloud computing technologies, the importance of pseudonym information has grown. However, the tools for verifying whether the de-identification methodology is correctly applied to ensure data confidentiality and usability are insufficient. This paper proposes a verification of de-identification techniques for personal healthcare information by considering data confidentiality and usability. Data are generated and preprocessed by considering the actual statistical data, personal information datasets, and de-identification datasets based on medical data to represent the de-identification technique as a numeric dataset. Five tree-based regression models (i.e., decision tree, random forest, gradient boosting machine, extreme gradient boosting, and light gradient boosting machine) are constructed using the de-identification dataset to effectively discover nonlinear relationships between dependent and independent variables in numerical datasets. Then, the most effective model is selected from personal information data in which pseudonym processing is essential for data utilization. The Shapley additive explanation, an explainable artificial intelligence technique, is applied to the most effective model to establish pseudonym processing policies and machine learning to present a machine-learning process that selects an appropriate de-identification methodology.

Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images

Sophisticated segmentation of the craniomaxillofacial bones (the mandible and maxilla) in computed tomography (CT) is essential for diagnosis and treatment planning for craniomaxillofacial surgeries. Conventional manual segmentation is time-consuming and challenging due to intrinsic properties of craniomaxillofacial bones and head CT such as the variance in the anatomical structures, low contrast of soft tissue, and artifacts caused by metal implants. However, data-driven segmentation methods, including deep learning, require a large consistent dataset, which creates a bottleneck in their clinical applications due to limited datasets. In this study, we propose a deep learning approach for the automatic segmentation of the mandible and maxilla in CT images and enhanced the compatibility for multi-center datasets. Four multi-center datasets acquired by various conditions were applied to create a scenario where the model was trained with one dataset and evaluated with the other datasets. For the neural network, we designed a hierarchical, parallel and multi-scale residual block to the U-Net (HPMR-U-Net). To evaluate the performance, segmentation with in-house dataset and with external datasets from multi-center were conducted in comparison to three other neural networks: U-Net, Res-U-Net and mU-Net. The results suggest that the segmentation performance of HPMR-U-Net is comparable to that of other models, with superior data compatibility.

Improved Deep Convolutional Neural Network to Classify Osteoarthritis from Anterior Cruciate Ligament Tear Using Magnetic Resonance Imaging

Anterior cruciate ligament (ACL) tear is caused by partially or completely torn ACL ligament in the knee, especially in sportsmen. There is a need to classify the ACL tear before it fully ruptures to avoid osteoarthritis. This research aims to identify ACL tears automatically and efficiently with a deep learning approach. A dataset was gathered, consisting of 917 knee magnetic resonance images (MRI) from Clinical Hospital Centre Rijeka, Croatia. The dataset we used consists of three classes: non-injured, partial tears, and fully ruptured knee MRI. The study compares and evaluates two variants of convolutional neural networks (CNN). We first tested the standard CNN model of five layers and then a customized CNN model of eleven layers. Eight different hyper-parameters were adjusted and tested on both variants. Our customized CNN model showed good results after a 25% random split using RMSprop and a learning rate of 0.001. The average evaluations are measured by accuracy, precision, sensitivity, specificity, and F1-score in the case of the standard CNN using the Adam optimizer with a learning rate of 0.001, i.e., 96.3%, 95%, 96%, 96.9%, and 95.6%, respectively. In the case of the customized CNN model, using the same evaluation measures, the model performed at 98.6%, 98%, 98%, 98.5%, and 98%, respectively, using an RMSprop optimizer with a learning rate of 0.001. Moreover, we also present our results on the receiver operating curve and area under the curve (ROC AUC). The customized CNN model with the Adam optimizer and a learning rate of 0.001 achieved 0.99 over three classes was highest among all. The model showed good results overall, and in the future, we can improve it to apply other CNN architectures to detect and segment other ligament parts like meniscus and cartilages.