Clinical validation of an automatic atlas‐based segmentation tool for male pelvis CT images

Abstract

This retrospective work aims to evaluate the possible impact on intraand inter-observer variability, contouring time, and contour accuracy of introducing a pelvis computed tomography (CT) auto-segmentation tool in radiotherapy planning workflow. Methods: Tests were carried out on five structures (bladder, rectum, pelvic lymph-nodes, and femoral heads) of six previously treated subjects, enrolling five radiation oncologists (ROs) to manually re-contour and edit auto-contours generated with a male pelvis CT atlas created with the commercial software MIM MAESTRO. The ROs first delineated manual contours (M). Then they modified the auto-contours, producing automatic-modified (AM) contours. The procedure was repeated to evaluate intra-observer variability, producing M1, M2, AM1, and AM2 contour sets (each comprising 5 structures × 6 test patients × 5 ROs = 150 contours), for a total of 600 contours. Potential time savings was evaluated by comparing contouring and editing times. Structure contours were compared to a reference standard by means of Dice similarity coefficient (DSC) and mean distance to agreement (MDA), to assess intra-and inter-observer variability. To exclude any automation bias, ROs evaluated both M and AM sets as "clinically acceptable" or "to be corrected" in a blind test. Results: Comparing AM to M sets, a significant reduction of both inter-observer variability (p < 0.001) and contouring time (-45% whole pelvis, p < 0.001) was obtained. Intra-observer variability reduction was significant only for bladder and femoral heads (p < 0.001). The statistical test showed no significant bias. Conclusion: Our atlas-based workflow proved to be effective for clinical practice as it can improve contour reproducibility and generate time savings. Based on these findings, institutions are encouraged to implement their autosegmentation method.

The clinical evaluation of atlas-based auto-segmentation for automatic contouring during cervical cancer radiotherapy

“…In our study, we found that a large atlas library size was not necessary for ABAS automatic contouring and an atlas library size of 20 could be enough to insure the accuracy of automatic contouring with the ABAS method. However, some authors validated ABAS based on a higher number of subjects for male pelvis CT images compared to what is concluded in the present article (20,21). Some authors validated ABAS based on a lower number of subjects for prostate cancers if compared to what is concluded in the present article (7,11).…”

Auto-segmentation for total marrow irradiation

“…The clinical validity of AI-defined OARs evaluated by Dice and HD95 has been assumed in prostate ( 39 ) including in physician-edited contours in a prospective study ( 40 ). The current study has demonstrated limited value in Dice and HD95, and therefore introduces a more stringent comparison metric, HD<1mm.…”

Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

“…51 Generally, good autosegmentation performance is achieved if results are as good or better than interobserver variability, which was shown to range from 0.8 to 0.99 for the pelvic region. 124 Consequently, Sun et al and Zhao et al demonstrated Dice score agreement greater than 0.89 for the pelvic region. 46,47 Recently, segmentation-specific studies have compared the performance of CBCT-derived sCT images for abdominal segmentations, reporting Dice scores above 0.8.…”