Comparison of Automated Atlas-Based Segmentation Software for Postoperative Prostate Cancer Radiotherapy

Delpon, G.; Escande, Alexandre; Ruef, T.; Darréon, J.; Fontaine, J.; Noblet, Caroline L.; Supiot, S.; Lacornerie, T.; Pasquier, David

doi:10.3389/fonc.2016.00178

Cited by 73 publications

(76 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the high contrast bony structures were the least affected (average DSC > 0.98). A similar result was found by Delpon et al that high attenuating structures, such as the pelvic bones, had a DSC of ~ 0.90 when compared to the physician delineations used in their study …”

Section: Discussionsupporting

confidence: 88%

Impact of CT reconstruction algorithm on auto‐segmentation performance

Miller

Mittelstaedt²,

Black

et al. 2019

J Applied Clin Med Phys

View full text Add to dashboard Cite

Model‐based iterative reconstruction (MBIR) reduces CT imaging dose while maintaining image quality. However, MBIR reduces noise while preserving edges which may impact intensity‐based tasks such as auto‐segmentation. This work evaluates the sensitivity of an auto‐contouring prostate atlas across multiple MBIR reconstruction protocols and benchmarks the results against filtered back projection (FBP). Images were created from raw projection data for 11 prostate cancer cases using FBP and nine different MBIR reconstructions (3 protocols/3 noise reduction levels) yielding 10 reconstructions/patient. Five bony structures, bladder, rectum, prostate, and seminal vesicles (SVs) were segmented using an auto‐segmentation pipeline that renders 3D binary masks for analysis. Performance was evaluated for volume percent difference (VPD) and Dice similarity coefficient (DSC), using FBP as the gold standard. Nonparametric Friedman tests plus post hoc all pairwise comparisons were employed to test for significant differences (P < 0.05) for soft tissue organs and protocol/level combinations. A physician performed qualitative grading of 396 MBIR contours across the prostate, bladder, SVs, and rectum in comparison to FBP using a six‐point scale. MBIR contours agreed with FBP for bony anatomy (DSC ≥ 0.98), bladder (DSC ≥ 0.94, VPD < 8.5%), and prostate (DSC = 0.94 ± 0.03, VPD = 4.50 ± 4.77% (range: 0.07–26.39%). Increased variability was observed for rectum (VPD = 7.50 ± 7.56% and DSC = 0.90 ± 0.08) and SVs (VPD and DSC of 8.23 ± 9.86% range (0.00–35.80%) and 0.87 ± 0.11, respectively). Over the all protocol/level comparisons, a significant difference was observed for the prostate VPD between BSPL1 and BSTL2 (adjusted P‐value = 0.039). Nevertheless, 300 of 396 (75.8%) of the four soft tissue structures using MBIR were graded as equivalent or better than FBP, suggesting that MBIR offered potential improvements in auto‐segmentation performance when compared to FBP. Future work may involve tuning organ‐specific MBIR parameters to further improve auto‐segmentation performance. Running title: Impact of CT Reconstruction Algorithm on Auto‐segmentation Performance.

show abstract

Section: Discussionsupporting

confidence: 88%

Impact of CT reconstruction algorithm on auto‐segmentation performance

Miller

Mittelstaedt²,

Black

et al. 2019

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…However, the different actions of initial contouring and of editing may entail the use of different tools, making a specific assessment of the impact of autocontouring more challenging. Similarly, the use of different editing tools makes it difficult to compare across studies . Recontouring the same cases may also introduce bias since familiarity with a case may reduce editing time.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the use of different editing tools makes it difficult to compare across studies. 6 Recontouring the same cases may also introduce bias since familiarity with a case may reduce editing time. Furthermore, manually drawn contours may also be considered to require editing when reviewed by other experts.…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of autocontouring in clinical practice: A practical method using the Turing test

et al. 2018

View full text Add to dashboard Cite

A better correspondence with time saving was observed for the misclassification rate than the quantitative contour measures explored. From this, we conclude that the inability to accurately judge the source of a contour indicates a reduced need for editing and therefore a greater time saving overall. Hence, task-based assessments of contouring performance may be considered as an additional way of evaluating the clinical utility of autosegmentation methods.

show abstract

“…The choice of a clinically available method for DIR gives an indication of performance achievable in the clinic, with only minor differences being observed between different commercial systems [58]. While the choice of algorithms may impact the absolute contouring performance, and more recent algorithms may result in better contours, it will do so for both selected atlases and perfect atlases.…”

Section: B Registration and Consensus Contour Generationmentioning

confidence: 99%

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

Schipaanboord

Boukerroui

Peressutti

et al. 2019

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

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.

show abstract

Comparison of Automated Atlas-Based Segmentation Software for Postoperative Prostate Cancer Radiotherapy

Cited by 73 publications

References 21 publications

Impact of CT reconstruction algorithm on auto‐segmentation performance

Impact of CT reconstruction algorithm on auto‐segmentation performance

Comparative evaluation of autocontouring in clinical practice: A practical method using the Turing test

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

Contact Info

Product

Resources

About