Comparison between manual and semi-automatic segmentation of nasal cavity and paranasal sinuses from CT images

Analysis of sphenoid sinus and ethmoid sinus volume and asymmetry by sex: A 3D-CT study

PURPOSE

To measure the volume of the sphenoid and ethmoid sinuses and to analyse the asymmetry index values by age/gender.

METHODS

Three-dimensional (3D) Computed Tomography (CT) images of 150 individuals (75 females, 75 males) of both sexes between the ages of 18-75 were included in our study. Sphenoid and ethmoid sinus volumes were measured using the 3D Slicer software package on these images, and the asymmetry index was calculated.

RESULTS

In our study, mean sphenoid sinus volume (female right: 4264.4 mm3, left: 3787.1 mm3; male right: 5201.1 mm3, left: 4818.2 mm3) and ethmoid sinus volume (female right: 3365.1 mm3, left: 3321.2 mm3; male right: 3440.9 mm3, left: 3459.5 mm3) were measured in males and females. Left sphenoid sinus values of males were statistically higher than females (p = 0.036). No statistically significant relationship existed between age, sinus volumes, and asymmetry index (p > 0.05). A statistically weak positive correlation existed between males' left sphenoid and ethmoid sinus volume (rho = 0.288; p = 0.012). There was no statistical relationship between asymmetry index in the whole group (p > 0.05). A statistically weak negative correlation was found between sphenoid and ethmoid sinus asymmetry index in males (rho=-0.352; p = 0.002). There was no statistical relationship between asymmetry index in females (p > 0.05).

CONCLUSION

Knowing paranasal sinus morphology, morphometry, and asymmetry index value will be significant for preoperative and postoperative periods.

Reliability and accuracy of a semi-automatic segmentation protocol of the nasal cavity using cone beam computed tomography in patients with sleep apnea

OBJECTIVES

The objectives of this study included using the cone beam computed tomography (CBCT) technology to assess: (1) intra- and inter-observer reliability of the volume measurement of the nasal cavity; (2) the accuracy of the segmentation protocol for evaluation of the nasal cavity.

MATERIALS AND METHODS

This study used test-retest reliability and accuracy methods within two different population sample groups, from Eastern Asia and North America. Thirty obstructive sleep apnea (OSA) patients were randomly selected from administrative and research oral health data archived at two dental faculties in China and Canada. To assess the reliability of the protocol, two observers performed nasal cavity volume measurement twice with a 10-day interval, using Amira software (v4.1, Visage Imaging Inc., Carlsbad, CA). The accuracy study used a computerized tomography (CT) scan of an OSA patient, who was not included in the study sample, to fabricate an anthropomorphic phantom of the nasal cavity volume with known dimensions (18.9 ml, gold standard). This phantom was scanned using one NewTom 5G (QR systems, Verona, Italy) CBCT scanner. The nasal cavity was segmented based on CBCT images and converted into standard tessellation language (STL) models. The volume of the nasal cavity was measured on the acquired STL models (18.99 ± 0.066 ml).

RESULTS

The intra-observer and inter-observer intraclass correlation coefficients for the volume measurement of the nasal cavity were 0.980-0.997 and 0.948-0.992 consecutively. The nasal cavity volume measurement was overestimated by 1.1%-3.1%, compared to the gold standard.

CONCLUSIONS

The semi-automatic segmentation protocol of the nasal cavity in patients with sleep apnea and by using cone beam computed tomography is reliable and accurate.

CLINICAL RELEVANCE

This study provides a reliable and accurate protocol for segmentation of nasal cavity, which will facilitate the clinician to analyze the images within nasoethmoidal region.

The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review

OBJECTIVE

To provide a comprehensive overview on the applications of artificial intelligence (AI) in rhinology, highlight its limitations, and propose strategies for its integration into surgical practice.

DATA SOURCES

Medline, Embase, CENTRAL, Ei Compendex, IEEE, and Web of Science.

REVIEW METHODS

English studies from inception until January 2022 and those focusing on any application of AI in rhinology were included. Study selection was independently performed by 2 authors; discrepancies were resolved by the senior author. Studies were categorized by rhinology theme, and data collection comprised type of AI utilized, sample size, and outcomes, including accuracy and precision among others.

CONCLUSIONS

An overall 5435 articles were identified. Following abstract and title screening, 130 articles underwent full-text review, and 59 articles were selected for analysis. Eleven studies were from the gray literature. Articles were stratified into image processing, segmentation, and diagnostics (n = 27); rhinosinusitis classification (n = 14); treatment and disease outcome prediction (n = 8); optimizing surgical navigation and phase assessment (n = 3); robotic surgery (n = 2); olfactory dysfunction (n = 2); and diagnosis of allergic rhinitis (n = 3). Most AI studies were published from 2016 onward (n = 45).

IMPLICATIONS FOR PRACTICE

This state of the art review aimed to highlight the increasing applications of AI in rhinology. Next steps will entail multidisciplinary collaboration to ensure data integrity, ongoing validation of AI algorithms, and integration into clinical practice. Future research should be tailored at the interplay of AI with robotics and surgical education.

Three-dimensional quantification of skeletal midfacial complex symmetry

PURPOSE

Quantification of skeletal symmetry in a healthy population could have a strong impact on the reconstructive surgical procedures where mirroring of the contralateral healthy side acts as a clinical reference for the restoration of unilateral defects. Hence, the aim of this study was to three-dimensionally assess the symmetry of skeletal midfacial complex in skeletal class I patients.

METHODS

A sample of 100 cone beam computed tomography (CBCT) scans (50 males, 50 females; age range: 19-40 years) were recruited. Automated segmentation of the skeletal midfacial complex was performed to create a three-dimensional (3D) virtual model using a convolutional neural network (CNN)-based segmentation tool. Thereafter, the segmented model was mirrored and registered to quantify skeletal symmetry using a color-coded conformance mapping based on a surface part comparison analysis.

RESULTS

Overall, the mean and root-mean-square (RMS) differences between complete true and mirrored models were 0.14 ± 0.12 and 0.87 ± 0.21 mm, respectively. Female patients had a significantly more symmetrical midfacial complex (mean difference: 0.11 ± 0.1 mm, RMS: 0.81 ± 0.17 mm) compared to male patients (mean difference: 0.16 ± 0.13 mm, RMS: 0.94 ± 0.23 mm). No significant difference existed between left and right sides irrespective of the patient's gender.

CONCLUSION

The comparison between true and mirrored complete and left/right split midfacial complex showed symmetry within a clinically acceptable range of 1 mm, which justifies the applicability of using the mirroring technique. The presented data could act as a reference guide for surgeons during planning of reconstructive surgical procedures and outcome assessment at follow-up.

Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images

The detection of maxillary sinus wall is important in dental fields such as implant surgery, tooth extraction, and odontogenic disease diagnosis. The accurate segmentation of the maxillary sinus is required as a cornerstone for diagnosis and treatment planning. This study proposes a deep learning-based method for fully automatic segmentation of the maxillary sinus, including clear or hazy states, on cone-beam computed tomographic (CBCT) images. A model for segmentation of the maxillary sinuses was developed using U-Net, a convolutional neural network, and a total of 19,350 CBCT images were used from 90 maxillary sinuses (34 clear sinuses, 56 hazy sinuses). Post-processing to eliminate prediction errors of the U-Net segmentation results increased the accuracy. The average prediction results of U-Net were a dice similarity coefficient (DSC) of 0.9090 ± 0.1921 and a Hausdorff distance (HD) of 2.7013 ± 4.6154. After post-processing, the average results improved to a DSC of 0.9099 ± 0.1914 and an HD of 2.1470 ± 2.2790. The proposed deep learning model with post-processing showed good performance for clear and hazy maxillary sinus segmentation. This model has the potential to help dental clinicians with maxillary sinus segmentation, yielding equivalent accuracy in a variety of cases.

Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images

An accurate three-dimensional (3D) segmentation of the maxillary sinus is crucial for multiple diagnostic and treatment applications. Yet, it is challenging and time-consuming when manually performed on a cone-beam computed tomography (CBCT) dataset. Recently, convolutional neural networks (CNNs) have proven to provide excellent performance in the field of 3D image analysis. Hence, this study developed and validated a novel automated CNN-based methodology for the segmentation of maxillary sinus using CBCT images. A dataset of 264 sinuses were acquired from 2 CBCT devices and randomly divided into 3 subsets: training, validation, and testing. A 3D U-Net architecture CNN model was developed and compared to semi-automatic segmentation in terms of time, accuracy, and consistency. The average time was significantly reduced (p-value < 2.2e−16) by automatic segmentation (0.4 min) compared to semi-automatic segmentation (60.8 min). The model accurately identified the segmented region with a dice similarity co-efficient (DSC) of 98.4%. The inter-observer reliability for minor refinement of automatic segmentation showed an excellent DSC of 99.6%. The proposed CNN model provided a time-efficient, precise, and consistent automatic segmentation which could allow an accurate generation of 3D models for diagnosis and virtual treatment planning.

Research Active Posterior Rhinomanometry Tomography Method for Nasal Breathing Determining Violations

This study analyzes the existing methods for studying nasal breathing. The aspects of verifying the results of rhinomanometric diagnostics according to the data of spiral computed tomography are considered, and the methodological features of dynamic posterior active rhinomanometry and the main indicators of respiration are also analyzed. The possibilities of testing respiratory olfactory disorders are considered, the analysis of errors in rhinomanometric measurements is carried out. In the conclusions, practical recommendations are given that have been developed for the design and operation of tools for functional diagnostics of nasal breathing disorders. It is advisable, according to the data of dynamic rhinomanometry, to assess the functioning of the nasal valve by the shape of the air flow rate signals during forced breathing and the structures of the soft palate by the residual nasopharyngeal pressure drop. It is imperative to take into account not only the maximum coefficient of aerodynamic nose drag, but also the values of the pressure drop and air flow rate in the area of transition to the turbulent quadratic flow regime. From the point of view of the physiology of the nasal response, it is necessary to look at the dynamic change to the current mode, given the hour of the forced response, so that it will ensure the maximum possible acidity in the legend. When planning functional rhinosurgical operations, it is necessary to apply the calculation method using computed tomography, which makes it possible to predict the functional result of surgery.

Axial-SpineGAN: simultaneous segmentation and diagnosis of multiple spinal structures on axial magnetic resonance imaging images

Providing a simultaneous segmentation and diagnosis of the spinal structures on axial magnetic resonance imaging (MRI) images has significant value for subsequent pathological analyses and clinical treatments. However, this task remains challenging, owing to the significant structural diversity, subtle differences between normal and abnormal structures, implicit borders, and insufficient training data. In this study, we propose an innovative network framework called 'Axial-SpineGAN' comprising a generator, discriminator, and diagnostor, aiming to address the above challenges, and to achieve simultaneous segmentation and disease diagnosis for discs, neural foramens, thecal sacs, and posterior arches on axial MRI images. The generator employs an enhancing feature fusion module to generate discriminative features, i.e. to address the challenges regarding the significant structural diversity and subtle differences between normal and abnormal structures. An enhancing border alignment module is employed to obtain an accurate pixel classification of the implicit borders. The discriminator employs an adversarial learning module to effectively strengthen the higher-order spatial consistency, and to avoid overfitting owing to insufficient training data. The diagnostor employs an automated diagnosis module to provide automated recognition of spinal diseases. Extensive experiments demonstrate that these modules have positive effects on improving the segmentation and diagnosis accuracies. Additionally, the results indicate that Axial-SpineGAN has the highest Dice similarity coefficient (94.9% ± 1.8%) in terms of the segmentation accuracy and highest accuracy rate (93.9% ± 2.6%) in terms of the diagnosis accuracy, thereby outperforming existing state-of-the-art methods. Therefore, our proposed Axial-SpineGAN is effective and potential as a clinical tool for providing an automated segmentation and disease diagnosis for multiple spinal structures on MRI images.

Novel three-dimensional methods to analyze the morphology of the nasal cavity and pharyngeal airway

OBJECTIVES

To assess the intraexaminer and interexaminer reliabilities of novel semiautomatic methods to segment the nasal cavity (NC) and pharyngeal airway (PA) and to determine the minimal cross-sectional area (CS) and hydraulic diameter (HD) of the PA.

MATERIALS AND METHODS

To test reproducibility, two examiners analyzed the NC and PA independently in 10 retrospectively selected cone beam computed tomography (CBCT) images using semiautomatic segmentation. The PA centerline was determined to assess the minimal CS and HD. The intraclass correlation coefficient (ICC) was used to calculate intraexaminer and interexaminer reliabilities. Measurement errors were assessed by Dahlberg's formula and paired t-tests. The level of agreement was assessed using the Bland-Altman method.

RESULTS

Intraexaminer and interexaminer reliabilities were excellent (minimal ICC, 0.960). The error of the method was good except for interexaminer values for the oropharynx (P = .016). The minimal CS and HD measurements were reliable (minimal ICC, 0.993; narrow limits of agreement).

CONCLUSIONS

The novel methods for analysis of the NC and PA are reliable. The minimal CS and HD demonstrated excellent reliabilities, which are critical to detect the most constricted part of the PA. Separation of the oropharynx from the voids close to the retroglossal area is not trivial and should be considered with caution.

Structural and functional radiomics for lung cancer

INTRODUCTION

Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes.

METHODS

Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer.

CONCLUSION

The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."

Preliminary Volumetric Calculation of the Mucosal Surface in the Nares of Lambs Using a Segmentation of Computed Tomographic Images

Intranasal vaccinations are becoming more important in both human and animal medicine to generate a localized IgA immune response not seen with parenteral vaccinations. This localized IgA response is more effective at reducing pathogen load on the mucosal surface of a potential host. One prerequisite for a successful nasal vaccination is the need to understand the distribution pattern of the nebulized vaccine, which requires an understanding the volume of the nares as well as the mucosal surface area. The exact mucosal surface area of ruminant nares has not yet been investigated. The aim of this concept study is to provide a detailed breakdown of a new method of volumetric rendering that can be used to calculate the volume and mucosal surface area of ruminant nares from computed tomographic images. The program Seg 3D was used to perform semi-automatic segmentation of a CT scan of a 9-month-old lamb head. Threshold segmentation and manual segmentation were used in combination to select the lamb's nasal cavity. The segmentation process yielded a volumetric rendering that was used to calculate the surface area and volume of the lamb's nasal cavity, with the segmentation process was repeated for each individual side of the lamb's nares. The surface area of the mucosal surface of each nostril is approximately 448 cm², and the volume is approximately 45 cm³. The methodology described in this study successfully calculated the volume and surface area of a lamb's nares using volumetric rendering.

Nasal cavities and the nasal septum: Anatomical variants and assessment of features with computed tomography

The nasal cavities are complex anatomical structures with high inter-individual variability that relates to different functions. Different anatomic variants may manifest at this site, mainly belonging to the nasal septum and turbinates. Precise knowledge of the anatomy and variants is fundamental for both radiologists and ENT surgeons. This article provides an overview of the main anatomic variants and their frequency, according to the existing literature, as well as ongoing research on nasal cavity segmentation in order to obtain personal 3D models and to predict post-surgical results.

Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization

Recent clinical research studies in forensic identification have highlighted the interest in sphenoid sinus anatomical characterization. Their pneumatization, well known as extremely variable in degrees and directions, could contribute to the radiologic identification, especially if dental records, fingerPrints, or DNA samples are not available. In this paper, we present a new approach for automatic person identification based on sphenoid sinus features extracted from computed tomography (CT) images of the skull. First, we present a new approach for fully automatic 3D reconstruction of the sphenoid hemisinuses which combines the fuzzy c-means method and mathematical morphology operations to detect and segment the object of interest. Second, deep shape features are extracted from both hemisinuses using a dilated residual version of a stacked convolutional auto-encoder. The obtained binary segmentation masks are thus hierarchically mapped into a compact and low-dimensional space preserving their semantic similarity. We finally employ the ℓ₂ distance to recognize the sphenoid sinus and therefore identify the person. This novel sphenoid sinus recognition method obtained 100% of identification accuracy when applied on a dataset composed of 85 CT scans stemming from 72 individuals. Automatic Forensic Identification using 3D Sphenoid Sinus Segmentation and Deep Characterization from Dilated Residual Auto-Encoders.

A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study

Background

Three-dimensional (3D) modeling of the nasal airway space is becoming increasingly important for assessment in breathing disorders. Processing cone beam computed tomography (CBCT) scans of this region is complicated, however, by the intricate anatomy of the sinuses compared to the simpler nasopharynx. A gold standard for these measures also is lacking. Previous work has shown that software programs can vary in accuracy and reproducibility outcomes of these measurements. This study reports the reproducibility and accuracy of an algorithm, airway segmentor (AS), designed for nasal airway space analysis using a 3D printed anthropomorphic nasal airway model.

Methods

To test reproducibility, two examiners independently used AS to edit and segment 10 nasal airway CBCT scans. The intra- and inter-examiner reproducibility of the nasal airway volume was evaluated using paired t-tests and intraclass correlation coefficients. For accuracy testing, the CBCT data for pairs of nasal cavities were 3D printed to form hollow shell models. The water-equivalent method was used to calculate the inner volume as the gold standard, and the models were then embedded into a dry human skull as a phantom and subjected to CBCT. AS, along with the software programs MIMICS 19.0 and INVIVO 5, was applied to calculate the inner volume of the models from the CBCT scan of the phantom. The accuracy was reported as a percentage of the gold standard.

Results

The intra-examiner reproducibility was high, and the inter-examiner reproducibility was clinically acceptable. AS and MIMICS presented accurate volume calculations, while INVIVO 5 significantly overestimated the mockup of the nasal airway volume.

Conclusion

With the aid of a 3D printing technique, the new algorithm AS was found to be a clinically reliable and accurate tool for the segmentation and reconstruction of the nasal airway space.

Sensitivity of nasal airflow variables computed via computational fluid dynamics to the computed tomography segmentation threshold

Computational fluid dynamics (CFD) allows quantitative assessment of transport phenomena in the human nasal cavity, including heat exchange, moisture transport, odorant uptake in the olfactory cleft, and regional delivery of pharmaceutical aerosols. The first step when applying CFD to investigate nasal airflow is to create a 3-dimensional reconstruction of the nasal anatomy from computed tomography (CT) scans or magnetic resonance images (MRI). However, a method to identify the exact location of the air-tissue boundary from CT scans or MRI is currently lacking. This introduces some uncertainty in the nasal cavity geometry. The radiodensity threshold for segmentation of the nasal airways has received little attention in the CFD literature. The goal of this study is to quantify how uncertainty in the segmentation threshold impacts CFD simulations of transport phenomena in the human nasal cavity. Three patients with nasal airway obstruction were included in the analysis. Pre-surgery CT scans were obtained after mucosal decongestion with oxymetazoline. For each patient, the nasal anatomy was reconstructed using three different thresholds in Hounsfield units (-800HU, -550HU, and -300HU). Our results demonstrate that some CFD variables (pressure drop, flowrate, airflow resistance) and anatomic variables (airspace cross-sectional area and volume) are strongly dependent on the segmentation threshold, while other CFD variables (intranasal flow distribution, surface area) are less sensitive to the segmentation threshold. These findings suggest that identification of an optimal threshold for segmentation of the nasal airway from CT scans will be important for good agreement between in vivo measurements and patient-specific CFD simulations of transport phenomena in the nasal cavity, particularly for processes sensitive to the transnasal pressure drop. We recommend that future CFD studies should always report the segmentation threshold used to reconstruct the nasal anatomy.

Accuracy and precision of manual segmentation of the maxillary sinus in MR images-a method study

OBJECTIVE

To assess the accuracy and precision of segmentation of the maxillary sinus in MR images to evaluate the potential usefulness of this modality in longitudinal studies of sinus development.

METHODS

A total of 15 healthy subjects who had been both craniofacial CT and MR scanned were included and the 30 maxillary sinus volumes were evaluated using segmentation. Two of the authors did segmentation of MRI and one of these authors did double segmentation. Agreement in results between CT and MRI as well as inter- and intraexaminer errors were evaluated by statistical and three-dimensional analysis.

RESULTS

The intraclass correlation coefficient for volume measurements for both method error, inter- and intraexaminer agreement were > 0.9 [maximal 95% confidence interval of 0.989-0.997, p < 0.001] and the limit of agreement for all parameters were < 5.1%. Segmentation errors were quantified in terms of overlap [Dice Coefficient (DICE) > 0.9 = excellent agreement] and border distance [95% percentile Hausdorff Distance (HD) < 2 mm = acceptable agreement]. The results were replicable and not influenced by systematic errors.

CONCLUSION

We found a high accuracy and precision of manual segmentation of the maxillary sinus in MR images. The largest mean errors were found close to the orbit and the teeth. Advances in knowledge: MRI can be used for 3D models of the paranasal sinuses with equally good results as CT and allows longitudinal follow-up of sinus development.

Volumetric assessment of sphenoid sinuses through segmentation on CT scan

PURPOSE

Computed tomography represents the gold standard for the assessment of morphological characteristics of sphenoid sinuses, whose anatomy has acquired a novel interest because of the recent introduction of transsphenoidal surgery and robot-assisted procedures. One of the most relevant parameters for planning surgical intervention is the volume of sphenoid sinuses, and with time few population studies have been published. However, at present, no data are available concerning the relation between volume and anatomical variants of sphenoid sinuses.

METHODS

We retrospectively evaluated head CT-scans of 100 patients (age range 25-99 years; mean age males 45.0; mean age females 50.5 years) to calculate the volume of sphenoid sinuses through automatic segmentation. Possible statistically significant differences according to sex and variants of pneumatization, and type of sinus were assessed, respectively, through Student's t test and one-way ANOVA test (p < 0.05).

RESULTS

Average volume of sphenoid sinuses in males was 10.005 ± 5.101 cm³, in females 7.920 ± 3.176 cm³. Differences according to sex were statistically significant (p < 0.05). Patients with pneumatization of pterygoid processes, dorsum sellae and anterior clinoid processes had a significantly higher volume than unaffected subjects. Moreover, differences of volume according to the type of sphenoid sinus were statistically significant (p < 0.05).

CONCLUSIONS

Results show that volume of sphenoid sinuses strongly depend upon the type of sinus and possible pneumatization variants. Moreover, the important of ethnic variability is confirmed.

A pilot study for segmentation of pharyngeal and sino-nasal airway subregions by automatic contour initialization

PURPOSE

The objective of the present study is to put forward a novel automatic segmentation algorithm to segment pharyngeal and sino-nasal airway subregions on 3D CBCT imaging datasets.

METHODS

A fully automatic segmentation of sino-nasal and pharyngeal airway subregions was implemented in MATLAB programing environment. The novelty of the algorithm is automatic initialization of contours in upper airway subregions. The algorithm is based on boundary definitions of the human anatomy along with shape constraints with an automatic initialization of contours to develop a complete algorithm which has a potential to enhance utility at clinical level. Post-initialization; five segmentation techniques: Chan-Vese level set (CVL), localized Chan-Vese level set (LCVL), Bhattacharya distance level set (BDL), Grow Cut (GC), and Sparse Field method (SFM) were used to test the robustness of automatic initialization.

RESULTS

Precision and F-score were found to be greater than 80% for all the regions with all five segmentation methods. High precision and low recall were observed with BDL and GC techniques indicating an under segmentation. Low precision and high recall values were observed with CVL and SFM methods indicating an over segmentation. A Larger F-score value was observed with SFM method for all the subregions. Minimum F-score value was observed for naso-ethmoidal and sphenoidal air sinus region, whereas a maximum F-score was observed in maxillary air sinuses region. The contour initialization was more accurate for maxillary air sinuses region in comparison with sphenoidal and naso-ethmoid regions.

CONCLUSION

The overall F-score was found to be greater than 80% for all the airway subregions using five segmentation techniques, indicating accurate contour initialization. Robustness of the algorithm needs to be further tested on severely deformed cases and on cases with different races and ethnicity for it to have global acceptance in Katradental radKatraiology workflow.

Two- and three-dimensional anatomy of paranasal sinuses in Arabian foals

The 2- and 3-dimensional (3D) anatomy and the morphometric properties of the paranasal sinuses of the foal have received little or no attention in the literature. The aim of this study was to obtain details of the paranasal sinuses using multiplane CT imaging to create 3D models and to determine morphological and morphometric data for the sinuses using the 3D models. The heads of five female foals were used in this study. The heads were scanned using computed tomography (CT) in the rostrocaudal direction. After the heads had been frozen, anatomical sections were obtained in the scan position. The 3D models of sinuses and the skull were prepared using MIMICS^®. These models were used to assess the surface area and volume of the sinuses, the width, height and orientation of the apertures connecting these sinuses and finally the planar relation of the sinuses with the skull. The right and left sides of all anatomical structures, except the sphenoid sinuses, had symmetric organization on CT images and anatomical sections. The total sinus surface area and volume on both sides were 214.4 cm² and 72.9 ml, respectively. The largest and the smallest sinuses were the frontal sinus (41.5 ml) and the middle conchal sinus (0.2 ml), respectively. It was found that the planes bounding the sinuses passed through easily palpable points on the head. In conclusion, 3D modeling in combination with conventional sectional imaging of the paranasal sinuses of the foal may help anatomists, radiologists, clinicians and veterinary students.

Patient specific CFD models of nasal airflow: overview of methods and challenges

Respiratory physiology and pathology are strongly dependent on the airflow inside the nasal cavity. However, the nasal anatomy, which is characterized by complex airway channels and significant individual differences, is difficult to analyze. Thus, commonly adopted diagnostic tools have yielded limited success. Nevertheless, with the rapid advances in computer resources, there have been more elaborate attempts to correlate airflow characteristics in human nasal airways with the symptoms and functions of the nose by computational fluid dynamics study. Furthermore, the computed nasal geometry can be virtually modified to reflect predicted results of the proposed surgical technique. In this article, several computational fluid mechanics (CFD) issues on patient-specific three dimensional (3D) modeling of nasal cavity and clinical applications were reviewed in relation to the cases of deviated nasal septum (decision for surgery), turbinectomy, and maxillary sinus ventilation (simulated- and post-surgery). Clinical relevance of fluid mechanical parameters, such as nasal resistance, flow allocation, wall shear stress, heat/humidity/NO gas distributions, to the symptoms and surgical outcome were discussed. Absolute values of such parameters reported by many research groups were different each other due to individual difference of nasal anatomy, the methodology for 3D modeling and numerical grid, laminar/turbulent flow model in CFD code. But, the correlation of these parameters to symptoms and surgery outcome seems to be obvious in each research group with subject-specific models and its variations (virtual- and post-surgery models). For the more reliable, patient-specific, and objective tools for diagnosis and outcomes of nasal surgery by using CFD, the future challenges will be the standardizations on the methodology for creating 3D airway models and the CFD procedures.

Intra-operative virtual endoscopy for image guided endonasal transsphenoidal pituitary surgery

PURPOSE

Virtual endoscopy has already proven its benefit for pre-operative planning of endoscopic pituitary surgery. The translation of such a system into the operating room is a logical consequence, but only a few general intra-operative image guided systems providing virtual endoscopic images have been proposed so far. A discussion of related visualization and interaction problems occurring during sinus and pituitary surgery is still missing.

METHODS

This paper aims at filling this gap and proposes a system that integrates an existing virtual endoscopy system originally designed for pre-operative planning of pituitary surgery with a professional intra-operative navigation system. Visualization and interaction possibilities of the pre-operative planning system have been extended to fulfill the special requirements to the system if used for intra-operative navigation of endonasal transsphenoidal pituitary surgery.

RESULTS

The feasibility of the system has been successfully tested on 1 cadaver and 12 patients. The virtual endoscopic images were found useful (1) during the endonasal transsphenoidal approach in cases of anatomic variations and for the individually tailored opening of the sellar floor, and (2) during tumor resection for respecting the internal carotid artery. The visualization of hidden anatomical structures behind the bony walls of the sphenoid sinus during the sellar phase of the surgery has been found most beneficial.

DISCUSSION

According to our data, intra-operative virtual endoscopy provides additional anatomical information to the surgeon. By depicting individual anatomical variations in advance, it may add to the safety of this frequent neurosurgical procedure.

CT-based manual segmentation and evaluation of paranasal sinuses

Manual segmentation of computed tomography (CT) datasets was performed for robot-assisted endoscope movement during functional endoscopic sinus surgery (FESS). Segmented 3D models are needed for the robots' workspace definition. A total of 50 preselected CT datasets were each segmented in 150-200 coronal slices with 24 landmarks being set. Three different colors for segmentation represent diverse risk areas. Extension and volumetric measurements were performed. Three-dimensional reconstruction was generated after segmentation. Manual segmentation took 8-10 h for each CT dataset. The mean volumes were: right maxillary sinus 17.4 cm(3), left side 17.9 cm(3), right frontal sinus 4.2 cm(3), left side 4.0 cm(3), total frontal sinuses 7.9 cm(3), sphenoid sinus right side 5.3 cm(3), left side 5.5 cm(3), total sphenoid sinus volume 11.2 cm(3). Our manually segmented 3D-models present the patient's individual anatomy with a special focus on structures in danger according to the diverse colored risk areas. For safe robot assistance, the high-accuracy models represent an average of the population for anatomical variations, extension and volumetric measurements. They can be used as a database for automatic model-based segmentation. None of the segmentation methods so far described provide risk segmentation. The robot's maximum distance to the segmented border can be adjusted according to the differently colored areas.