Evaluation of the accuracy of automated segmentation based on deep learning for prostate cancer patients

Purpose: This study evaluated the accuracy of a commercial deep learning (DL)-based algorithm for segmenting the prostate, seminal vesicles (SV), and organs at risk (OAR) in patients with prostate cancer. Methods: Ten patients with prostate cancer were selected to compare automated and manual segmentation. The prostate, SV, and OAR, including the bladder, rectum, left and right femoral heads, and penile bulb, were delineated and reviewed according to our institutional protocols by radiation oncologists. The CT and MR images were fused to the prostate, and the prostate and penile bulb were manually delineated on the CT and MR images. The remaining organs were delineated on the CT images without the MR images. MVision AI Contour + was used to perform DL-based automated segmentation. The dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95%) were evaluated for comparison with manual delineations. Results: The mean DSC values for the prostate, SV, bladder, rectum, both femoral heads, and penile bulb were 0.86, 0.80, 0.96, 0.92, 0.97, and 0.64, respectively. The HD95% for all the organs was less than 3 mm. Conclusions: The commercial DL-based auto segmentation solution provided high-quality contours in patients with prostate cancer. (c) 2024 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.