Atlas-based Automatic Segmentation Research Articles

Comparison of commercial atlas-based automatic segmentation software for prostate radiotherapy treatment planning.

This study aims to assess the accuracy of automatic atlas-based contours for various key anatomical structures in prostate radiotherapy treatment planning. The evaluated structures include the bladder, rectum, prostate, seminal vesicles, femoral heads and penile bulb. CT images from 20 patients who underwent intensity-modulated radiotherapy were randomly chosen to create an atlas library. Atlas contours of the seven anatomical structures were generated using four software packages: ABAS, Eclipse, MIM, and RayStation. These contours were then compared to manual delineations performed by oncologists, which served as the ground truth. Evaluation metrics such as dice similarity coefficient (DSC), mean distance to agreement (MDA), and volume ratio (VR) were calculated to assess the accuracy of the contours. Additionally, the time taken by each software to generate the atlas contour was recorded. The mean DSC values for the bladder exhibited strong agreement (>0.8) with manual delineations for all software except for Eclipse and RayStation. Similarly, the femoral heads showed significant similarity between the atlas contours and ground truth across all software, with mean DSC values exceeding 0.9 and MDA values close to zero. On the other hand, the penile bulb displayed only moderate agreement with the ground truth, with mean DSC values ranging from 0.5 to 0.7 for all software. A similar trend was observed in the prostate atlas contours, except for MIM, which achieved a mean DSC of over 0.8. For the rectum, both ABAS and MIM atlases demonstrated strong agreement with the ground truth, resulting in mean DSC values of more than 0.8. Overall, MIM and ABAS outperformed Eclipse and RayStation in both DSC and MDA. These results indicate that the atlas-based segmentation employed in this study produces acceptable contours for the anatomical structures of interest in prostate radiotherapy treatment planning.

Automatic Segmentation of Parkinson Disease Therapeutic Targets Using Nonlinear Registration and Clinical MR Imaging: Comparison of Methodology, Presence of Disease, and Quality Control

Introduction: Accurate and precise delineation of the globus pallidus pars interna (GPi) and subthalamic nucleus (STN) is critical for the clinical treatment and research of Parkinson’s disease (PD). Automated segmentation is a developing technology which addresses limitations of visualizing deep nuclei on MR imaging and standardizing their definition in research applications. We sought to compare manual segmentation with three workflows for template-to-patient nonlinear registration providing atlas-based automatic segmentation of deep nuclei. Methods: Bilateral GPi, STN, and red nucleus (RN) were segmented for 20 PD and 20 healthy control (HC) subjects using 3T MRIs acquired for clinical purposes. The automated workflows used were an option available in clinical practice and two common research protocols. Quality control (QC) was performed on registered templates via visual inspection of readily discernible brain structures. Manual segmentation using T1, proton density, and T2 sequences was used as “ground truth” data for comparison. Dice similarity coefficient (DSC) was used to assess agreement between segmented nuclei. Further analysis was done to compare the influences of disease state and QC classifications on DSC. Results: Automated segmentation workflows (CIT-S, CRV-AB, and DIST-S) had the highest DSC for the RN and lowest for the STN. Manual segmentations outperformed automated segmentation for all workflows and nuclei; however, for 3/9 workflows (CIT-S STN, CRV-AB STN, and CRV-AB GPi) the differences were not statically significant. HC and PD only showed significant differences in 1/9 comparisons (DIST-S GPi). QC classification only demonstrated significantly higher DSC in 2/9 comparisons (CRV-AB RN and GPi). Conclusion: Manual segmentations generally performed better than automated segmentations. Disease state does not appear to have a significant effect on the quality of automated segmentations via nonlinear template-to-patient registration. Notably, visual inspection of template registration is a poor indicator of the accuracy of deep nuclei segmentation. As automatic segmentation methods continue to evolve, efficient and reliable QC methods will be necessary to support safe and effective integration into clinical workflows.

Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy.

Contouring organs at risk (OARs) is a time-intensive task that is a critical part of radiation therapy. Atlas-based automatic segmentation has shown some success at reducing this time burden on practitioners; however, this method often requires significant manual editing to reach a clinically accurate standard. Deep learning (DL) auto-segmentation has recently emerged as a promising solution. This study compares the accuracy of DL and atlas-based auto-segmentation in relation to clinical 'gold standard' reference contours. Ninety CT datasets (30 head and neck, 30 thoracic, 30 pelvic) were automatically contoured using both atlas and DL segmentation techniques. Sixteen critical OARs were then quantitatively measured for accuracy using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). Qualitative analysis was performed to visually classify the accuracy of each structure into one of four explicitly defined categories. Additionally, the time to edit atlas and DL contours to a clinically acceptable level was recorded for a subset of 9 OARs. Of the 16 OARs analysed, DL delivered statistically significant improvements over atlas segmentation in 13 OARs measured with DSC, 12 OARs measured with HD, and 12 OARs measured qualitatively. The mean editing time for the subset of DL contours was 50%, 23% and 61% faster (all P < 0.05) than that of atlas segmentation for the head and neck, thorax, and pelvis respectively. Deep learning segmentation comprehensively outperformed atlas-based contouring for the majority of evaluated OARs. Improvements were observed in geometric accuracy and visual acceptability, while editing time was reduced leading to increased workflow efficiency.

Educative Impact of Automatic Delineation Applied to Head and Neck Cancer Patients on Radiation Oncology Residents.

To evaluate the educational impact on radiation oncology residents in training when introducing an automatic segmentation software in head and neck cancer patients regarding organs at risk (OARs) and prophylactic cervical lymph node level (LNL) volumes. Two cases treated by exclusive intensity-modulated radiotherapy were delineated by an expert radiation oncologist and were considered as reference. Then, these cases were delineated by residents divided into two groups: group 1 (control group), experienced residents delineating manually, group 2 (experimental group), young residents on their first rotation trained with automatic delineation, delineating manually first (M -) and then after using the automatic system (M +). The delineation accuracy was assessed using the Overlap Volume (OV). Regarding the OARs, mean OV was 0.62 (SD = 0.05) for group 1, 0.56 (SD = 0.04) for group 2M - , and 0.61 (SD = 0.03) for group 2M + . Mean OV was higher in group 1 compared to group 2M - (p = 0.01). There was no OV difference between group 1 and group 2M + (p = 0.67). Mean OV was higher in the group 2M + compared to group 2M - (p < 0.003). Regarding LNL, mean OV was 0.53 (SD = 0.06) in group 1, 0.54 (SD = 0.03) in group 2M - , and 0.58 (SD = 0.04) in group 2M + . Mean OV was higher in group 2M + for 11 of the 12 analysed structures compared to group 2M - (p = 0.016). Prior use of the automatic delineation software reduced the average contouring time per case by 34 to 40%. Prior use of atlas-based automatic segmentation reduces the delineation duration, and provides reliable OARs and LNL delineations.

Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models

BackgroundContour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the delineation efficiency and reduce inter-observer variation. Additionally, automated segmentation using artificial intelligence (AI) has recently become available. In this study, auto-segmentations by atlas- and AI-based models for Organs at Risk (OAR) in patients with prostate and head and neck cancer were performed and delineation accuracies were evaluated.MethodsTwenty-one patients with prostate cancer and 30 patients with head and neck cancer were evaluated. MIM Maestro was used to apply the atlas-based segmentation. MIM Contour ProtégéAI was used to apply the AI-based segmentation. Three similarity indices, the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance to agreement (MDA), were evaluated and compared with manual delineations. In addition, radiation oncologists visually evaluated the delineation accuracies.ResultsAmong patients with prostate cancer, the AI-based model demonstrated higher accuracy than the atlas-based on DSC, HD, and MDA for the bladder and rectum. Upon visual evaluation, some errors were observed in the atlas-based delineations when the boundary between the small bowel or the seminal vesicle and the bladder was unclear. For patients with head and neck cancer, no significant differences were observed between the two models for almost all OARs, except small delineations such as the optic chiasm and optic nerve. The DSC tended to be lower when the HD and the MDA were smaller in small volume delineations.ConclusionsIn terms of efficiency, the processing time for head and neck cancers was much shorter than manual delineation. While quantitative evaluation with AI-based segmentation was significantly more accurate than atlas-based for prostate cancer, there was no significant difference for head and neck cancer. According to the results of visual evaluation, less necessity of manual correction in AI-based segmentation indicates that the segmentation efficiency of AI-based model is higher than that of atlas-based model. The effectiveness of the AI-based model can be expected to improve the segmentation efficiency and to significantly shorten the delineation time.

Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy.

Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets—the so-called organs-at-risk (OARs). An optimal RT planning benefits from the accurate segmentation of the gross tumor volume and surrounding OARs. Manual segmentation is a time-consuming and tedious task for radiation oncologists. Therefore, it is crucial to develop automatic image segmentation to relieve radiation oncologists of the tedious contouring work. Currently, the atlas-based automatic segmentation technique is commonly used in clinical routines. However, this technique depends heavily on the similarity between the atlas and the image segmented. With significant advances made in computer vision, deep learning as a part of artificial intelligence attracts increasing attention in medical image automatic segmentation. In this article, we reviewed deep learning based automatic segmentation techniques related to lung cancer and compared them with the atlas-based automatic segmentation technique. At present, the auto-segmentation of OARs with relatively large volume such as lung and heart etc. outperforms the organs with small volume such as esophagus. The average Dice similarity coefficient (DSC) of lung, heart and liver are over 0.9, and the best DSC of spinal cord reaches 0.9. However, the DSC of esophagus ranges between 0.71 and 0.87 with a ragged performance. In terms of the gross tumor volume, the average DSC is below 0.8. Although deep learning based automatic segmentation techniques indicate significant superiority in many aspects compared to manual segmentation, various issues still need to be solved. We discussed the potential issues in deep learning based automatic segmentation including low contrast, dataset size, consensus guidelines, and network design. Clinical limitations and future research directions of deep learning based automatic segmentation were discussed as well.

Application of an automatic segmentation method for evaluating cardiac structure doses received by breast radiotherapy patients.

Quantifying radiation dose to cardiac substructures is important for research on the etiology and prevention of complications following radiotherapy; however, segmentation of substructures is challenging. In this study we demonstrate the application of our atlas-based automatic segmentation method to breast cancer radiotherapy plans for generating radiation doses in support of late effects research. We applied our segmentation method to contour heart substructures on the computed tomography (CT) images of 70 breast cancer patients who received external photon radiotherapy. Two cardiologists provided manual segmentation of the whole heart (WH), left/right atria, left/right ventricles, and left anterior descending artery (LAD). The automatically contours were compared with manual delineations to evaluate similarity in terms of geometry and dose. The mean Dice similarity coefficient between manual and automatic segmentations was 0.96 for the WH, 0.65 to 0.82 for the atria and ventricles, and 0.06 for the LAD. The mean average surface distance was 1.2mm for the WH, 3.4 to 4.1mm for the atria and ventricles, and 6.4mm for the LAD. We found the dose to the cardiac substructures based on our automatic segmentation agrees with manual segmentation within expected observer variability. For left breast patients, the mean absolute difference in mean dose was 0.1Gy for the WH, 0.2 to 0.7Gy for the atria and ventricles, and 1.8Gy for the LAD. For right breast patients, these values were 0.0Gy, 0.1 to 0.4Gy, and 0.4Gy, respectively. Our automatic segmentation method will facilitate the development of radiotherapy prescriptive criteria for mitigating cardiovascular complications.

Educative Impact Of Automatic Head And Neck Cancer Patients Delineation On Radiation Oncology Residents

To evaluate the educational impact on radiation oncology residents in training when introducing an automatic segmentation software. Were assessed accuracy, consistency and speed of delineation regarding organs at risk (OARs) and prophylactic cervical lymph node levels (LNL) volumes in head and neck cancer patients. Two cases of head and neck cancers patients treated by exclusive intensity-modulated radiotherapy were delineated by an expert radiation oncologist and were considered as reference. Then, these two cases were delineated by residents divided into three groups: Group 1 (control group) composed of more experienced residents had never been exposed to automatic delineation system delineated manually, Group 2 (experimental group) composed of young residents on their first rotation trained with automatic delineation system during their learning period delineating manually prior (M-) and after using the automatic system (M+). The delineation accuracy was assessed using the Overlap Volume (OV) index for the OARs and LNL. The time needed to complete the delineation of the two cases was measured. Regarding the OARs, mean OV was 0.62 (SD = 0.05) for group 1, 0.56 (SD = 0.04) for group 2 M-, and 0.61 (SD = 0.03) for group 2 M+. Mean OV was higher in group 1 compared to group 2 M- (p = 0.01). There were no OV difference between group 1 and group 2 M+ (p = 0.67). The mean OV was higher in the group 2 M+ compared to the group 2 M- after paired analysis (p<0.003). Regarding LNL, mean OV was 0.53 (SD = 0.06) in group 1, 0.54 (SD = 0.03) in group 2 M-, and 0.58 (SD = 0.04) in group 2 M+. There was no significant difference between the groups despite a favorable trend in favor of the group 2 M+ (p = 0.079). Mean OV was higher in group 2 M+ for 11 of the 12 structures analyzed compared to group 2 M- (p = 0.016). Prior use of the automatic delineation software reduced the average contouring time per case by 34 to 40%. In this study of head and neck cancer delineation, prior use of atlas-based automatic segmentation by residents reduced the delineation duration, and led to reliable OARs and LNL delineations.

Regional SUV quantification in hybrid PET/MR, a comparison of two atlas-based automatic brain segmentation methods

BackgroundQuantitative analysis of brain positron-emission tomography (PET) depends on structural segmentation, which can be time-consuming and operator-dependent when performed manually. Previous automatic segmentation usually registered subjects’ images onto an atlas template (defined as RSIAT here) for group analysis, which changed the individuals’ images and probably affected regional PET segmentation. In contrast, we could register atlas template to subjects’ images (RATSI), which created an individual atlas template and may be more accurate for PET segmentation. We segmented two representative brain areas in twenty Parkinson disease (PD) and eight multiple system atrophy (MSA) patients performed in hybrid positron-emission tomography/magnetic resonance imaging (PET/MR). The segmentation accuracy was evaluated using the Dice coefficient (DC) and Hausdorff distance (HD), and the standardized uptake value (SUV) measurements of these two automatic segmentation methods were compared, using manual segmentation as a reference.ResultsThe DC of RATSI increased, and the HD decreased significantly (P < 0.05) compared with the RSIAT in PD, while the results of one-way analysis of variance (ANOVA) found no significant differences in the SUVmean and SUVmax among the two automatic and the manual segmentation methods. Further, RATSI was used to compare regional differences in cerebral metabolism pattern between PD and MSA patients. The SUVmean in the segmented cerebellar gray matter for the MSA group was significantly lower compared with the PD group (P < 0.05), which is consistent with previous reports.ConclusionThe RATSI was more accurate for the caudate nucleus and putamen automatic segmentation and can be used for regional PET analysis in hybrid PET/MR.

Atlas Sampling for Prone Breast Automatic Segmentation of Organs at Risk: The Importance of Patients' Body Mass Index and Breast Cup Size for an Optimized Contouring of the Heart and the Coronary Vessels.

Objective:Delineation of organs at risk is a time-consuming task. This study evaluates the benefits of using single-subject atlas-based automatic segmentation of organs at risk in patients with breast cancer treated in prone position, with 2 different criteria for choosing the atlas subject. Together with laterality (left/right), the criteria used were either (1) breast volume or (2) body mass index and breast cup size.Methods:An atlas supporting different selection criteria for automatic segmentation was generated from contours drawn by a senior radiation oncologist (RO_A). Atlas organs at risk included heart, left anterior descending artery, and right coronary artery. Manual contours drawn by RO_A and automatic segmentation contours of organs at risk and breast clinical target volume were created for 27 nonatlas patients. A second radiation oncologist (RO_B) manually contoured (M_B) the breast clinical target volume and the heart. Contouring times were recorded and the reliability of the automatic segmentation was assessed in the context of 3-D planning.Results:Accounting for body mass index and breast cup size improved automatic segmentation results compared to breast volume-based sampling, especially for the heart (mean similarity indexes >0.9 for automatic segmentation organs at risk and clinical target volume after RO_A editing). Mean similarity indexes for the left anterior descending artery and the right coronary artery edited by RO_A expanded by 1 cm were ≥0.8. Using automatic segmentation reduced contouring time by 40%. For each parameter analyzed (eg, D2%), the difference in dose, averaged over all patients, between automatic segmentation structures edited by RO_A and the same structure manually drawn by RO_A was <1.5% of the prescribed dose. The mean heart dose was reliable for the unedited heart segmentation, and for right-sided treatments, automatic segmentation was adequate for treatment planning with 3-D conformal tangential fields.Conclusions:Automatic segmentation for prone breast radiotherapy stratified by body mass index and breast cup size improved segmentation accuracy for the heart and coronary vessels compared to breast volume sampling. A significant reduction in contouring time can be achieved by using automatic segmentation.

An Evaluation of Atlas Selection Methods for Atlas-Based Automatic Segmentation in Radiotherapy Treatment Planning.

Atlas-based automatic segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed as a way to improve the accuracy and execution time of segmentation, assuming that, the more similar the atlas is to the patient, the better the results will be. This paper presents an analysis of atlas selection methods in the context of radiotherapy treatment planning. For a range of commonly contoured OARs, a thorough comparison of a large class of typical atlas selection methods has been performed. For this evaluation, clinically contoured CT images of the head and neck ( N=316 ) and thorax ( N=280 ) were used. The state-of-the-art intensity and deformation similarity-based atlas selection methods were found to compare poorly to perfect atlas selection. Counter-intuitively, atlas selection methods based on a fixed set of representative atlases outperformed atlas selection methods based on the patient image. This study suggests that atlas-based segmentation with currently available selection methods compares poorly to the potential best performance, hampering the clinical utility of atlas-based segmentation. Effective atlas selection remains an open challenge in atlas-based segmentation for radiotherapy planning.

EP-1709: Can atlas-based automatic segmentation contour H&N OARs like a physician?

EP-1709: Can atlas-based automatic segmentation contour H&N OARs like a physician?

EP-1739: The feasibility of atlas-based automatic segmentation of MRI for prostate radiotherapy planning

EP-1739: The feasibility of atlas-based automatic segmentation of MRI for prostate radiotherapy planning

EP-1886: The feasibility of atlas-based automatic segmentation of MRI for H&N radiotherapy planning

EP-1886: The feasibility of atlas-based automatic segmentation of MRI for H&N radiotherapy planning

Landmark Guided, Atlas-Based Automatic Segmentation of Brainstem from Magnetic Resonance Neuroimages

Landmark Guided, Atlas-Based Automatic Segmentation of Brainstem from Magnetic Resonance Neuroimages

Investigation Into a New Quality Assurance Tool for Radiation Therapy Structure Delineation: Atlas-Based Automatic Segmentation (ABAS) of Cardiac Structures for Non-Small Cell Lung Cancer From RTOG 0617

To evaluate the feasibility of the ABAS for the automatic delineation of cardiac structures (pericardium, atria, ventricles) when compared with the manually contoured cardiac structures. Furthermore, we explored using the ABAS contouring as a quality assurance (QA) tool.

Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation

BackgroundIntensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions.MethodsThe updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for “manual to automatic” and “manual to corrected” volumes comparisons.ResultsIn both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors.ConclusionsThe updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert.

Assessing the Accuracy of Atlas-based Automatic Segmentation of Volumes for Adaptive Treatment in the Head and Neck

Assessing the Accuracy of Atlas-based Automatic Segmentation of Volumes for Adaptive Treatment in the Head and Neck

SU-E-J-102: The Impact of the Number of Subjects for Atlas-Based Automatic Segmentation.

To determine the impact of atlas size on the performance of atlas-based automatic segmentation (ABAS) in delineation of organs at risk for adaptive radiation therapy. A total of 25 patients who had undergone intensity modulated radiation therapy for various head and neck cancers were retrospectively selected for inclusion in a library to be used for ABAS with the MIM VISTA software package (MIM Software, Cleveland OH). Treatment planning computed tomography (CT) scans and subsequent organ at risk (OAR) contours generated as part of the treatment planning process for these patients were added to the library. This library of 25 patients was then successively pruned to generate 5 atlases with 25, 20, 15, 10, and 5 patient subjects respectively. Atlas based segmentation was performed on 10 retrospectively selected treatment planning CT scans to automatically generate right and left parotid glands and brainstem contours. These planning CT scans belonged to a unique set of 10 patient subjects different from the ones used for generating the atlases. One physician (JW), who was blinded to the ABAS results, manually delineated gold-standard contours for the right and left parotid glands and brainstem. Dice similarity coefficients were calculated and analyzed as a function of atlas subject size. For the sites selected in this study, the performance of ABAS was relatively insensitive to atlas size. Furthermore, some patient subjects were repeatedly selected implying that the adoption of a single standard patient for ABAS may be of benefit. Our preliminary results indicate that the performance of the atlas based segmentation module in MIM VISTA Version 5.2 for the organs studied here may be relatively insensitive to the atlas size.

Atlas-based automatic mouse brain image segmentation revisited: model complexity vs. image registration

Although many atlas-based segmentation methods have been developed and validated for the human brain, limited work has been done for the mouse brain. This paper investigated roles of image registration and segmentation model complexity in the mouse brain segmentation. We employed four segmentation models [single atlas, multiatlas, simultaneous truth and performance level estimation (STAPLE) and Markov random field (MRF) via four different image registration algorithms (affine, B-spline free-form deformation (FFD), Demons and large deformation diffeomorphic metric mapping (LDDMM)] for delineating 19 structures from in vivo magnetic resonance microscopy images. We validated their accuracies against manual segmentation. Our results revealed that LDDMM outperformed Demons, FFD and affine in any of the segmentation models. Under the same registration, increasing segmentation model complexity from single atlas to multiatlas, STAPLE or MRF significantly improved the segmentation accuracy. Interestingly, the multiatlas-based segmentation using nonlinear registrations (FFD, Demons and LDDMM) had similar performance to their STAPLE counterparts, while they both outperformed their MRF counterparts. Furthermore, when the single-atlas affine segmentation was used as reference, the improvement due to nonlinear registrations (FFD, Demons and LDDMM) in the single-atlas segmentation model was greater than that due to increasing model complexity (multiatlas, STAPLE and MRF affine segmentation). Hence, we concluded that image registration plays a more crucial role in the atlas-based automatic mouse brain segmentation as compared to model complexity. Multiple atlases with LDDMM can best improve the segmentation accuracy in the mouse brain among all segmentation models tested in this study.