Auto-segmentation of the brachial plexus assessed with TaCTICS – A software platform for rapid multiple-metric quantitative evaluation of contours

Awan, Musaddiq J.; Dyer, Brandon A.; Kalpathy–Cramer, Jayashree; Bongers, E.; Dahele, Max; Yang, Jinzhong; Walker, Gary V.; Thaker, Nikhil G.; Holliday, Emma B.; Bishop, Andrew J.; Thomas, Charles R.; Rosenthal, David I.; Fuller, Clifton D.

doi:10.3109/0284186x.2014.953638

Cited by 4 publications

(7 citation statements)

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“…After reviewing their abstracts, 49 were considered to be relevant and were further supplemented with selected publications from their list of references. In total, we collected 75 publications 22–96 focused on RT planning and three studies focused on hyperthermia therapy planning 97–99 from 2008 to date (Fig. 1), along with three review papers related to auto‐segmentation in the H&N region 19–21 .…”

Section: Resultsmentioning

confidence: 99%

“…However, it is a time‐consuming (e.g., 3–6 hours per image for up to 20 OARs 19,87,98 ), tedious, and costly task that is limited by the subjective human interpretation of organ boundaries, which is manifested through the intra‐ and interobserver variability in the delineation (Table V). Most studies therefore rely on a single set of ground truth per image, nevertheless, studies report also two, 32,60,63,79,88,93 three, 22,25,41,58,75,97,99 four, 71,98 five 77 , or even eight 89 independently obtained sets of ground truth per image. An anatomically validated ground truth was introduced for a single OAR, that is, the brachial plexus, 6,121 so that its manual delineations obtained from high‐resolution MR images of up to 12 cadavers were first validated by dissection and then registered to corresponding CT images to obtain the ground truth for the purpose of RT planning.…”

Section: Resultsmentioning

confidence: 99%

“…Atlas 27,29,34,44,52,58,59,61,68,69,71,73,[78][79][80][81]84,85,87,[89][90][91]93,94,99 with shape/appearance models 38,66,76,77,82,86,92,95 with intensity models [97][98][99] with feature classification 35,63,72,75,83,86 with contour refinement 72,76,92 with level set refinement 91 Feature classification 64,74 Localization model and feature classification 51,56 Level-set statistical model 88,…”

Section: Methodsmentioning

confidence: 99%

“…91 In 2015 § , six different teams participated in a challenge to segment the brainstem, mandible, optic chiasm, optic nerves, parotid glands, and ASD (mm) ASSD: 1.4 AE 0.4, 32 1.8 AE 0.4 (supraglottic) 32 ; DTA91 m;f : 91 m;f (o = 5,p = 10,S) 77 Trachea DC (%) 91 m;f (o = 2,p = 12) 63 Cochlea DC (%) 78 AE 8 (o = 2,p = 24,•), 60 76 AE 9 (o = 2,p = 8), 60 91 m;f (o = 5,p = 10,S), 77 50 AE 13, 32 Table VII does not report comparisons to the ground truth that was obtained by manually corrected or merged auto-segmentation results. 32,80,96 In the case the results were reported separately for multiple versions of a method Variations of the volumetric coefficient (VC) TPR Sensitivity 24,31,40,41,50,55,56,59,67,68,71,90,93,94,96 jA\Bj jAj TNR Specificity 41,93,94,96 jðA[BÞ C j jA C j PPV Positive predictive value (inclusion) 24,31,40,55,56,68 jA\Bj jBj FDR False discovery rate (segmented volume) 50,59 jBnAj jBj…”

Section: G Segmentation Performancementioning

confidence: 99%

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

et al. 2020

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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 modalityboth computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OARthe 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 databaseseveral 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; methodologycurrent methods have shifted from atlas-based to deep learning autosegmentation, which is expected to become even more sophisticated; ground truthdelineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metricsthe 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 performancethe 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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: G Segmentation Performancementioning

confidence: 99%

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

et al. 2020

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“…It is difficult to define a DSC or 95% HD threshold at which DCs are felt to be clinically useful since manual contours from different clinicians will inevitably demonstrate some amount of IOV (21,25). Additionally, Cha et al investigated prostate radiotherapy DCs in the clinical workflow and found that geometric indices when comparing the unedited and final contours were not strongly correlated with contouring times or (27)(28)(29)(30), and/or reported on the IOV of manual contours (21,25,28). These studies are compiled in Table 3 alongside our results.…”

Section: Discussionmentioning

confidence: 99%

Training and Validation of Deep Learning-Based Auto-Segmentation Models for Lung Stereotactic Ablative Radiotherapy Using Retrospective Radiotherapy Planning Contours

Wong¹,

Huang²,

Giambattista

et al. 2021

Front. Oncol.

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PurposeDeep learning-based auto-segmented contour (DC) models require high quality data for their development, and previous studies have typically used prospectively produced contours, which can be resource intensive and time consuming to obtain. The aim of this study was to investigate the feasibility of using retrospective peer-reviewed radiotherapy planning contours in the training and evaluation of DC models for lung stereotactic ablative radiotherapy (SABR).MethodsUsing commercial deep learning-based auto-segmentation software, DC models for lung SABR organs at risk (OAR) and gross tumor volume (GTV) were trained using a deep convolutional neural network and a median of 105 contours per structure model obtained from 160 publicly available CT scans and 50 peer-reviewed SABR planning 4D-CT scans from center A. DCs were generated for 50 additional planning CT scans from center A and 50 from center B, and compared with the clinical contours (CC) using the Dice Similarity Coefficient (DSC) and 95% Hausdorff distance (HD).ResultsComparing DCs to CCs, the mean DSC and 95% HD were 0.93 and 2.85mm for aorta, 0.81 and 3.32mm for esophagus, 0.95 and 5.09mm for heart, 0.98 and 2.99mm for bilateral lung, 0.52 and 7.08mm for bilateral brachial plexus, 0.82 and 4.23mm for proximal bronchial tree, 0.90 and 1.62mm for spinal cord, 0.91 and 2.27mm for trachea, and 0.71 and 5.23mm for GTV. DC to CC comparisons of center A and center B were similar for all OAR structures.ConclusionsThe DCs developed with retrospective peer-reviewed treatment contours approximated CCs for the majority of OARs, including on an external dataset. DCs for structures with more variability tended to be less accurate and likely require using a larger number of training cases or novel training approaches to improve performance. Developing DC models from existing radiotherapy planning contours appears feasible and warrants further clinical workflow testing.

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Radiothérapie conformationnelle avec modulation d’intensité des cancers des voies aérodigestives supérieures. Dose de tolérance des tissus sains : moelle épinière et plexus brachial

Boisselier

Racadot

Thariat

et al. 2016

Cancer/Radiothérapie

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Auto-segmentation of the brachial plexus assessed with TaCTICS – A software platform for rapid multiple-metric quantitative evaluation of contours

Cited by 4 publications

References 23 publications

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

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

Training and Validation of Deep Learning-Based Auto-Segmentation Models for Lung Stereotactic Ablative Radiotherapy Using Retrospective Radiotherapy Planning Contours

Radiothérapie conformationnelle avec modulation d’intensité des cancers des voies aérodigestives supérieures. Dose de tolérance des tissus sains : moelle épinière et plexus brachial

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