Comparing Deep Learning Based Prostate Delineation With Manual Contouring From Multiple Experts

Abstract

Accurate contouring of the target is very important in radiation therapy treatment planning. Unlike most other cancers where the radiation target occupies a small area of an organ, prostate cancer treatment involves the irradiation of the entire prostate. This study evaluates the quality of prostate contours generated from a deep learning segmentation (DLSEG) model.A total of 23 prostate cancer patients who did not underwent prostatectomy was randomly selected. The original contours used clinically was carefully quality checked and revised when necessary by one expert radiation oncologist (RO) and regarded as the ground truth (GT). Two additional ROs were invited to perform manual contouring of the prostate from scratch. Auto contours (labelled as AI) generated by a research version of DLSEG was then compared with those from manual delineations. A paired t-test was employed for statistical comparison. A comparative geometric analysis of the manual and automatic delineations was performed to quantify the multi-observer delineation variability, including dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD).Using GT contour as reference, the contours from three additional RO achieved an average of 0.81 ± 0.04 for DSC, 0.60 ± 0.16cm and 0.21 ± 0.05cm for HD95 and MSD, respectively. In contrast, AI contours achieved an average of 0.83 ± 0.05 for DSC, 0.60 ± 0.18cm and 0.21 ± 0.07 cm for HD95 and MSD, respectively. No statistical difference was found for the delineation accuracy between the three observers (P > 0.05). AI contours were considered to have similar performance to the three human experts with all geometric metrics (P > 0.05).The DLSEG model for prostate contouring tested demonstrates similar performance in terms of accuracy compared with expert ROs.