Reinforcement learning-based anatomical maps for pancreas subregion and duct segmentation.

BACKGROUND: The pancreas is a complex abdominal organ with many anatomical variations, and therefore automated pancreas segmentation from medical images is a challenging application. PURPOSE: In this paper, we present a framework for segmenting individual pancreatic subregions and the pancreatic duct from three-dimensional (3D) computed tomography (CT) images. METHODS: A multiagent reinforcement learning (RL) network was used to detect landmarks of the head, neck, body, and tail of the pancreas, and landmarks along the pancreatic duct in a selected target CT image. Using the landmark detection results, an atlas of pancreases was nonrigidly registered to the target image, resulting in anatomical probability maps for the pancreatic subregions and duct. The probability maps were augmented with multilabel 3D U-Net architectures to obtain the final segmentation results. RESULTS: To evaluate the performance of our proposed framework, we computed the Dice similarity coefficient (DSC) between the predicted and ground truth manual segmentations on a database of 82 CT images with manually segmented pancreatic subregions and 37 CT images with manually segmented pancreatic ducts. For the four pancreatic subregions, the mean DSC improved from 0.38, 0.44, and 0.39 with standard 3D U-Net, Attention U-Net, and shifted windowing (Swin) U-Net architectures, to 0.51, 0.47, and 0.49, respectively, when utilizing the proposed RL-based framework. For the pancreatic duct, the RL-based framework achieved a mean DSC of 0.70, significantly outperforming the standard approaches and existing methods on different datasets. CONCLUSIONS: The resulting accuracy of the proposed RL-based segmentation framework demonstrates an improvement against segmentation with standard U-Net architectures.

HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge.

BACKGROUND AND PURPOSE: To promote the development of auto-segmentation methods for head and neck (HaN) radiation treatment (RT) planning that exploit the information of computed tomography (CT) and magnetic resonance (MR) imaging modalities, we organized HaN-Seg: The Head and Neck Organ-at-Risk CT and MR Segmentation Challenge. MATERIALS AND METHODS: The challenge task was to automatically segment 30 organs-at-risk (OARs) of the HaN region in 14 withheld test cases given the availability of 42 publicly available training cases. Each case consisted of one contrast-enhanced CT and one T1-weighted MR image of the HaN region of the same patient, with up to 30 corresponding reference OAR delineation masks. The performance was evaluated in terms of the Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95), and statistical ranking was applied for each metric by pairwise comparison of the submitted methods using the Wilcoxon signed-rank test. RESULTS: While 23 teams registered for the challenge, only seven submitted their methods for the final phase. The top-performing team achieved a DSC of 76.9 % and a HD95 of 3.5 mm. All participating teams utilized architectures based on U-Net, with the winning team leveraging rigid MR to CT registration combined with network entry-level concatenation of both modalities. CONCLUSION: This challenge simulated a real-world clinical scenario by providing non-registered MR and CT images with varying fields-of-view and voxel sizes. Remarkably, the top-performing teams achieved segmentation performance surpassing the inter-observer agreement on the same dataset. These results set a benchmark for future research on this publicly available dataset and on paired multi-modal image segmentation in general.

vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images.

BACKGROUND: Accurate and consistent contouring of organs-at-risk (OARs) from medical images is a key step of radiotherapy (RT) cancer treatment planning. Most contouring approaches rely on computed tomography (CT) images, but the integration of complementary magnetic resonance (MR) modality is highly recommended, especially from the perspective of OAR contouring, synthetic CT and MR image generation for MR-only RT, and MR-guided RT. Although MR has been recognized as valuable for contouring OARs in the head and neck (HaN) region, the accuracy and consistency of the resulting contours have not been yet objectively evaluated. PURPOSE: To analyze the interobserver and intermodality variability in contouring OARs in the HaN region, performed by observers with different level of experience from CT and MR images of the same patients. METHODS: In the final cohort of 27 CT and MR images of the same patients, contours of up to 31 OARs were obtained by a radiation oncology resident (junior observer, JO) and a board-certified radiation oncologist (senior observer, SO). The resulting contours were then evaluated in terms of interobserver variability, characterized as the agreement among different observers (JO and SO) when contouring OARs in a selected modality (CT or MR), and intermodality variability, characterized as the agreement among different modalities (CT and MR) when OARs were contoured by a selected observer (JO or SO), both by the Dice coefficient (DC) and 95-percentile Hausdorff distance (HD 95 $_{95}$ ). RESULTS: The mean (±standard deviation) interobserver variability was 69.0 ± 20.2% and 5.1 ± 4.1 mm, while the mean intermodality variability was 61.6 ± 19.0% and 6.1 ± 4.3 mm in terms of DC and HD 95 $_{95}$ , respectively, across all OARs. Statistically significant differences were only found for specific OARs. The performed MR to CT image registration resulted in a mean target registration error of 1.7 ± 0.5 mm, which was considered as valid for the analysis of intermodality variability. CONCLUSIONS: The contouring variability was, in general, similar for both image modalities, and experience did not considerably affect the contouring performance. However, the results indicate that an OAR is difficult to contour regardless of whether it is contoured in the CT or MR image, and that observer experience may be an important factor for OARs that are deemed difficult to contour. Several of the differences in the resulting variability can be also attributed to adherence to guidelines, especially for OARs with poor visibility or without distinctive boundaries in either CT or MR images. Although considerable contouring differences were observed for specific OARs, it can be concluded that almost all OARs can be contoured with a similar degree of variability in either the CT or MR modality, which works in favor of MR images from the perspective of MR-only and MR-guided RT.

HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset.

PURPOSE: For the cancer in the head and neck (HaN), radiotherapy (RT) represents an important treatment modality. Segmentation of organs-at-risk (OARs) is the starting point of RT planning, however, existing approaches are focused on either computed tomography (CT) or magnetic resonance (MR) images, while multimodal segmentation has not been thoroughly explored yet. We present a dataset of CT and MR images of the same patients with curated reference HaN OAR segmentations for an objective evaluation of segmentation methods. ACQUISITION AND VALIDATION METHODS: The cohort consists of HaN images of 56 patients that underwent both CT and T1-weighted MR imaging for image-guided RT. For each patient, reference segmentations of up to 30 OARs were obtained by experts performing manual pixel-wise image annotation. By maintaining the distribution of patient age and gender, and annotation type, the patients were randomly split into training Set 1 (42 cases or 75%) and test Set 2 (14 cases or 25%). Baseline auto-segmentation results are also provided by training the publicly available deep nnU-Net architecture on Set 1, and evaluating its performance on Set 2. DATA FORMAT AND USAGE NOTES: The data are publicly available through an open-access repository under the name HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Dataset. Images and reference segmentations are stored in the NRRD file format, where the OAR filenames correspond to the nomenclature recommended by the American Association of Physicists in Medicine, and OAR and demographics information is stored in separate comma-separated value  files. POTENTIAL APPLICATIONS: The HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge is launched in parallel with the dataset release to promote the development of automated techniques for OAR segmentation in the HaN. Other potential applications include out-of-challenge algorithm development and benchmarking, as well as external validation of the developed algorithms.

Special Section Guest Editorial: Advances in High-Dimensional Medical Image Processing.

Guest Editors Ivana Isgum, Bennett A. Landman, and Tomaz Vrtovec introduce the JMI Special Section on Advances in High-Dimensional Medical Image Processing.

A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation.

PURPOSE: To propose a fully automated deep learning (DL) framework for the vertebral morphometry and Cobb angle measurement from three-dimensional (3D) computed tomography (CT) images of the spine, and validate the proposed framework on an external database. METHODS: The vertebrae were first localized and segmented in each 3D CT image using a DL architecture based on an ensemble of U-Nets, and then automated vertebral morphometry in the form of vertebral body (VB) and intervertebral disk (IVD) heights, and spinal curvature measurements in the form of coronal and sagittal Cobb angles (thoracic kyphosis and lumbar lordosis) were performed using dedicated machine learning techniques. The framework was trained on 1725 vertebrae from 160 CT images and validated on an external database of 157 vertebrae from 15 CT images. RESULTS: The resulting mean absolute errors (± standard deviation) between the obtained DL and corresponding manual measurements were 1.17 ± 0.40 mm for VB heights, 0.54 ± 0.21 mm for IVD heights, and 3.42 ± 1.36° for coronal and sagittal Cobb angles, with respective maximal absolute errors of 2.51 mm, 1.64 mm, and 5.52°. Linear regression revealed excellent agreement, with Pearson's correlation coefficient of 0.943, 0.928, and 0.996, respectively. CONCLUSION: The obtained results are within the range of values, obtained by existing DL approaches without external validation. The results therefore confirm the scalability of the proposed DL framework from the perspective of application to external data, and time and computational resource consumption required for framework training.

Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification.

Morphological abnormalities of the femoroacetabular (hip) joint are among the most common human musculoskeletal disorders and often develop asymptomatically at early easily treatable stages. In this paper, we propose an automated framework for landmark-based detection and quantification of hip abnormalities from magnetic resonance (MR) images. The framework relies on a novel idea of multi-landmark environment analysis with reinforcement learning. In particular, we merge the concepts of the graphical lasso and Morris sensitivity analysis with deep neural networks to quantitatively estimate the contribution of individual landmark and landmark subgroup locations to the other landmark locations. Convolutional neural networks for image segmentation are utilized to propose the initial landmark locations, and landmark detection is then formulated as a reinforcement learning (RL) problem, where each landmark-agent can adjust its position by observing the local MR image neighborhood and the locations of the most-contributive landmarks. The framework was validated on T1-, T2- and proton density-weighted MR images of 260 patients with the aim to measure the lateral center-edge angle (LCEA), femoral neck-shaft angle (NSA), and the anterior and posterior acetabular sector angles (AASA and PASA) of the hip, and derive the quantitative abnormality metrics from these angles. The framework was successfully tested using the UNet and feature pyramid network (FPN) segmentation architectures for landmark proposal generation, and the deep Q-network (DeepQN), deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and actor-critic policy gradient (A2C) RL networks for landmark position optimization. The resulting overall landmark detection error of 1.5 mm and angle measurement error of 1.4° indicates a superior performance in comparison to existing methods. Moreover, the automatically estimated abnormality labels were in 95% agreement with those generated by an expert radiologist.

Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation.

PURPOSE: To summarize and critically evaluate the existing studies for spinopelvic measurements of sagittal balance that are based on deep learning (DL). METHODS: Three databases (PubMed, WoS and Scopus) were queried for records using keywords related to DL and measurement of sagittal balance. After screening the resulting 529 records that were augmented with specific web search, 34 studies published between 2017 and 2022 were included in the final review, and evaluated from the perspective of the observed sagittal spinopelvic parameters, properties of spine image datasets, applied DL methodology and resulting measurement performance. RESULTS: Studies reported DL measurement of up to 18 different spinopelvic parameters, but the actual number depended on the image field of view. Image datasets were composed of lateral lumbar spine and whole spine X-rays, biplanar whole spine X-rays and lumbar spine magnetic resonance cross sections, and were increasing in size or enriched by augmentation techniques. Spinopelvic parameter measurement was approached either by landmark detection or structure segmentation, and U-Net was the most frequently applied DL architecture. The latest DL methods achieved excellent performance in terms of mean absolute error against reference manual measurements (~ 2° or ~ 1 mm). CONCLUSION: Although the application of relatively complex DL architectures resulted in an improved measurement accuracy of sagittal spinopelvic parameters, future methods should focus on multi-institution and multi-observer analyses as well as uncertainty estimation and error handling implementations for integration into the clinical workflow. Further advances will enhance the predictive analytics of DL methods for spinopelvic parameter measurement. LEVEL OF EVIDENCE I: Diagnostic: individual cross-sectional studies with the consistently applied reference standard and blinding.

Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods.

Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/interobserver variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning-based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modality - both computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OAR - the spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image database - several image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodology - current methods have shifted from atlas-based to deep learning auto-segmentation, which is expected to become even more sophisticated; ground truth - delineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metrics - the Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performance - the best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.

A deep learning tool for fully automated measurements of sagittal spinopelvic balance from X-ray images: performance evaluation.

PURPOSE: The purpose of this study is to evaluate the performance of a novel deep learning (DL) tool for fully automated measurements of the sagittal spinopelvic balance from X-ray images of the spine in comparison with manual measurements. METHODS: Ninety-seven conventional upright sagittal X-ray images from 55 subjects were retrospectively included in this study. Measurements of the parameters of the sagittal spinopelvic balance, i.e., the sacral slope (SS), pelvic tilt (PT), spinal tilt (ST), pelvic incidence (PI) and spinosacral angle (SSA), were obtained manually by identifying specific anatomical landmarks using the SurgiMap Spine software and by the fully automated DL tool. Statistical analysis was performed in terms of the mean absolute difference (MAD), standard deviation (SD) and Pearson correlation, while the paired t test was used to search for statistically significant differences between manual and automated measurements. RESULTS: The differences between reference manual measurements and those obtained automatically by the DL tool were, respectively, for SS, PT, ST, PI and SSA, equal to 5.0° (3.4°), 2.7° (2.5°), 1.2° (1.2°), 5.5° (4.2°) and 5.0° (3.5°) in terms of MAD (SD), with a statistically significant corresponding Pearson correlation of 0.73, 0.90, 0.95, 0.81 and 0.71. No statistically significant differences were observed between the two types of measurement (p value always above 0.05). CONCLUSION: The differences between measurements are in the range of the observer variability of manual measurements, indicating that the DL tool can provide clinically equivalent measurements in terms of accuracy but superior measurements in terms of cost-effectiveness, reliability and reproducibility.

Computer-assisted pedicle screw trajectory planning using CT-inferred bone density: A demonstration against surgical outcomes.

PURPOSE: Image-guided spine surgery and preoperative computer-assisted planning provide spine surgeons with tools to improve the safety, accuracy, and reliability of pedicle screw placement. The purpose of this study is to demonstrate a computer-assisted pedicle screw placement planning tool in comparison to screws as delivered by a spine surgeon. METHODS: We describe a novel computer-assisted tool for preoperative pedicle screw placement planning in computed tomography (CT) images, designed with respect to the vertebral shape and structure, and augmented with respect to the considerations of surgical practice. The approach is based on three-dimensional (3D) modeling of the vertebral body and pedicles, and planning of the pedicle screw size and insertion trajectory by maximizing the screw fastening strength, evaluated through CT-inferred bone density maps. The approach is augmented by yielding screw plans consistent with the straight-forward surgical technique of aligning screws parallel to vertebral endplates, and the screw entry points following the spinal curvature to facilitate rod attachment. For a cohort of 25 patients, placement plans were retrospectively obtained for 204 pedicle screws with the computer-assisted tool from preoperative CT images, while reference trajectories of inserted pedicle screws were reconstructed in 3D from postoperative biplanar radiographs. RESULTS: The best performing version of the computer-assisted tool achieved clinically acceptable preoperative pedicle screw placement plans in 96.6% of the cases, while the comparison to the postoperative reconstructions resulted in 3.4 ± 2.5 mm for the screw entry point location, 2.7 ± 1.6 mm for the screw crossing point location, and 7.4 ± 5.3∘ for the screw sagittal inclination (mean absolute difference ± standard deviation). CONCLUSION: Quantitative comparison revealed that the preoperative placement plans are consistent with the postoperative results, and that the computer-assisted tool integrating bone density and surgical constraints can successfully incorporate important aspects of pedicle screw placement. The results therefore confirm the accuracy of the tool prior to being integrated in an image-guidance system.

Three-dimensional pelvic incidence is much higher in (thoraco)lumbar scoliosis than in controls.

PURPOSE: The pelvic incidence (PI) is used to describe the sagittal spino-pelvic alignment. In previous studies, radiographs were used, leading to less accuracy in establishing the three-dimensional (3D) spino-pelvic parameters. The purpose of this study is to analyze the differences in the 3D sagittal spino-pelvic alignment in adolescent idiopathic scoliosis (AIS) subjects and non-scoliotic controls. METHODS: Thirty-seven female AIS patients that underwent preoperative supine low-dose computed tomography imaging of the spine, hips and pelvis as part of their general workup were included and compared to 44 non-scoliotic age-matched female controls. A previously validated computerized method was used to measure the PI in 3D, as the angle between the line orthogonal to the inclination of the sacral endplate and the line connecting the center of the sacral endplate with the hip axis. RESULTS: The PI was on average 46.8° ± 12.4° in AIS patients and 41.3° ± 11.4° in controls (p = 0.025), with a higher PI in Lenke type 5 curves (50.6° ± 16.2°) as compared to controls (p = 0.042), whereas the Lenke type 1 curves (45.9° ± 12.2°) did not differ from controls (p = 0.141). CONCLUSION: Lenke type 5 curves show a significantly higher PI than controls, whereas the Lenke type 1 curves did not differ from controls. This suggests a role of pelvic morphology and spino-pelvic alignment in the pathogenesis of idiopathic scoliosis. Further longitudinal studies should explore the exact role of the PI in the initiation and progression of different AIS types. These slides can be retrieved under Electronic Supplementary Material.

Variability Analysis of Manual and Computer-Assisted Preoperative Thoracic Pedicle Screw Placement Planning.

STUDY DESIGN: A comparison among preoperative pedicle screw placement plans, obtained from computed tomography (CT) images manually by two spine surgeons and automatically by a computer-assisted method. OBJECTIVE: To analyze and compare the manual and computer-assisted approach to pedicle screw placement planning in terms of the inter- and intraobserver variability. SUMMARY OF BACKGROUND DATA: Several methods for computer-assisted pedicle screw placement planning have been proposed; however, a systematic variability analysis against manual planning has not been performed yet. METHODS: For 256 pedicle screws, preoperative placement plans were determined manually by two experienced spine surgeons, each independently performing two sets of measurements by using a dedicated software for surgery planning. For the same 256 pedicle screws, preoperative placement plans were also obtained automatically by a computer-assisted method that was based on modeling of the vertebral structures in 3D, which were used to determine the pedicle screw size and insertion trajectory by maximizing its fastening strength through the underlying bone mineral density. RESULTS: A total of 1024 manually (2 observers × 2 sets × 256 screws) and 256 automatically (1 computer-assisted method × 256 screws) determined preoperative pedicle screw placement plans were obtained and compared in terms of the inter- and intraobserver variability. A large difference was observed for the pedicle screw sagittal inclination that was, in terms of the mean absolute difference and the corresponding standard deviation, equal to 18.3°â€Š±â€Š7.6° and 12.3°â€Š±â€Š6.5°, respectively for the intraobserver variability of the second observer and for the interobserver variability between the first observer and the computer-assisted method. CONCLUSION: The interobserver variability among the observers and the computer-assisted method is within the intraobserver variability of each observer, which indicates on the potential use of the computer-assisted approach as a useful tool for spine surgery that can be adapted according to the preferences of the surgeon. LEVEL OF EVIDENCE: 3.

Segmentation of parotid glands from registered CT and MR images.

PURPOSE: To develop an automatic multimodal method for segmentation of parotid glands (PGs) from pre-registered computed tomography (CT) and magnetic resonance (MR) images and compare its results to the results of an existing state-of-the-art algorithm that segments PGs from CT images only. METHODS: Magnetic resonance images of head and neck were registered to the accompanying CT images using two different state-of-the-art registration procedures. The reference domains of registered image pairs were divided on the complementary PG regions and backgrounds according to the manual delineation of PGs on CT images, provided by a physician. Patches of intensity values from both image modalities, centered around randomly sampled voxels from the reference domain, served as positive or negative samples in the training of the convolutional neural network (CNN) classifier. The trained CNN accepted a previously unseen (registered) image pair and classified its voxels according to the resemblance of its patches to the patches used for training. The final segmentation was refined using a graph-cut algorithm, followed by the dilate-erode operations. RESULTS: Using the same image dataset, segmentation of PGs was performed using the proposed multimodal algorithm and an existing monomodal algorithm, which segments PGs from CT images only. The mean value of the achieved Dice overlapping coefficient for the proposed algorithm was 78.8%, while the corresponding mean value for the monomodal algorithm was 76.5%. CONCLUSIONS: Automatic PG segmentation on the planning CT image can be augmented with the MR image modality, leading to an improved RT planning of head and neck cancer.

A computerized method for evaluating scoliotic deformities using elliptical pattern recognition in X-ray spine images.

BACKGROUND AND OBJECTIVE: Several studies have evaluated the reproducibility of the Cobb angle for measuring the degree of scoliotic deformities from X-ray spine images, and proposed different geometric models for describing the spinal curvature. The ellipse was shown to be an adequate geometric form, but was not yet applied for the identification and quantification of scoliotic curvatures. The purpose of this study is therefore to propose and validate a novel computerized methodology for the detection of elliptical patterns from X-ray images to evaluate the extent of the underlying scoliotic deformity. METHODS: For anteroposterior each X-ray spine image, the spine curve is first reconstructed from vertebral centroids. The ellipse that best fits to the obtained spine curve is the found within a least square and genetic algorithm optimization framework. The geometric parameters of the resulting best fit ellipse are finally used to define an index that quantifies the spinal curvature. RESULTS: The proposed methodology was validated on three synthetic images and then successfully applied to 20 clinical anteroposterior X-ray spine images of patients with a different degree of scoliotic deformity, with the resulting maximal relative error of 3% for the synthetic images and an overall error of 0.5 ±â€¯0.4 mm (mean ±â€¯standard deviation) for the clinical cases. CONCLUSIONS: The results indicate that the proposed computerized methodology is able to reliably reproduce scoliotic curvatures using the geometric parameters of the underlying ellipses. In comparison to conventional approaches, the proposed methodology potentially produces less errors, requires a relatively low observer interaction, takes into account all vertebrae within the observed scoliotic deformity, and allows for both qualitative and quantitative evaluations that may complement the diagnosis, study and treatment of scoliosis.

Vertebral End-Plate Perforation for Intervertebral Disc Height Preservation After Single-level Lumbar Discectomy: A Randomized-controlled Trial.

STUDY DESIGN: Pilot single-centre, stratified, prospective, randomized, double-blinded, parallel-group, controlled study. OBJECTIVE: To determine whether vertebral end-plate perforation after lumbar discectomy causes annulus reparation and intervertebral disc volume restoration. To determine that after 6 months there would be no clinical differences between the control and study group. SUMMARY OF BACKGROUND DATA: Low back pain is the most common long-term complication after lumbar discectomy. It is mainly caused by intervertebral disc space loss, which promotes progressive degeneration. This is the first study to test the efficiency of a previously described method (vertebral end-plate perforation) that should advocate for annulus fibrosus reparation and disc space restoration. METHODS: We selected 30 eligible patients according to inclusion and exclusion criteria and randomly assigned them to the control (no end-plate perforation) or study (end-plate perforation) group. Each patient was evaluated in 5 different periods, where data were collected [preoperative and 6-mo follow-up magnetic resonance imaging and functional outcome data: visual analogue scale (VAS) back, VAS legs, Oswestry disability index (ODI)]. Intervertebral space volume (ISV) and height (ISH) were measured form the magnetic resonance images. Statistical analysis was performed using paired t test and linear regression. P<0.05 was considered statistically significant. RESULTS: We found no statistically significant difference between the control group and the study group concerning ISV (P=0.6808) and ISH (P=0.8981) 6 months after surgery. No statistically significant differences were found between ODI, VAS back, and VAS legs after 6 months between the 2 groups, however, there were statistically significant differences between these parameters in different time periods. Correlation between the volume of disc tissue removed and preoperative versus postoperative difference in ISV was statistically significant (P=0.0020). CONCLUSIONS: The present study showed positive correlation between the volume of removed disc tissue and decrease in postoperative ISV and ISH. There were no statistically significant differences in ISV and ISH between the group with end-plate perforation and the control group 6 months after lumbar discectomy. Clinical outcome and disability were significantly improved in both groups 3 and 6 months after surgery.

Segmentation of Pathological Structures by Landmark-Assisted Deformable Models.

Computerized segmentation of pathological structures in medical images is challenging, as, in addition to unclear image boundaries, image artifacts, and traces of surgical activities, the shape of pathological structures may be very different from the shape of normal structures. Even if a sufficient number of pathological training samples are collected, statistical shape modeling cannot always capture shape features of pathological samples as they may be suppressed by shape features of a considerably larger number of healthy samples. At the same time, landmarking can be efficient in analyzing pathological structures but often lacks robustness. In this paper, we combine the advantages of landmark detection and deformable models into a novel supervised multi-energy segmentation framework that can efficiently segment structures with pathological shape. The framework adopts the theory of Laplacian shape editing, that was introduced in the field of computer graphics, so that the limitations of statistical shape modeling are avoided. The performance of the proposed framework was validated by segmenting fractured lumbar vertebrae from 3-D computed tomography images, atrophic corpora callosa from 2-D magnetic resonance (MR) cross-sections and cancerous prostates from 3D MR images, resulting respectively in a Dice coefficient of 84.7 ± 5.0%, 85.3 ± 4.8% and 78.3 ± 5.1%, and boundary distance of 1.14 ± 0.49mm, 1.42 ± 0.45mm and 2.27 ± 0.52mm. The obtained results were shown to be superior in comparison to existing deformable model-based segmentation algorithms.

Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge.

The evaluation of changes in Intervertebral Discs (IVDs) with 3D Magnetic Resonance (MR) Imaging (MRI) can be of interest for many clinical applications. This paper presents the evaluation of both IVD localization and IVD segmentation methods submitted to the Automatic 3D MRI IVD Localization and Segmentation challenge, held at the 2015 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2015) with an on-site competition. With the construction of a manually annotated reference data set composed of 25 3D T2-weighted MR images acquired from two different studies and the establishment of a standard validation framework, quantitative evaluation was performed to compare the results of methods submitted to the challenge. Experimental results show that overall the best localization method achieves a mean localization distance of 0.8 mm and the best segmentation method achieves a mean Dice of 91.8%, a mean average absolute distance of 1.1 mm and a mean Hausdorff distance of 4.3 mm, respectively. The strengths and drawbacks of each method are discussed, which provides insights into the performance of different IVD localization and segmentation methods.

A benchmark for comparison of dental radiography analysis algorithms.

Dental radiography plays an important role in clinical diagnosis, treatment and surgery. In recent years, efforts have been made on developing computerized dental X-ray image analysis systems for clinical usages. A novel framework for objective evaluation of automatic dental radiography analysis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2015 Bitewing Radiography Caries Detection Challenge and Cephalometric X-ray Image Analysis Challenge. In this article, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the dental anatomy data repository of bitewing radiographs, the creation of the anatomical abnormality classification data repository of cephalometric radiographs, and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, seven automatic methods for analysing cephalometric X-ray image and two automatic methods for detecting bitewing radiography caries have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic dental radiography analysis is still a challenging and unsolved problem. The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/~cweiwang/ISBI2015/).