Open-source deep-learning models for segmentation of normal structures for prostatic and gynecological high-dose-rate brachytherapy: Comparison of architectures.

Background: The use of deep learning-based auto-contouring algorithms in various treatment planning services is increasingly common. There is a notable deficit of commercially or publicly available models trained on large or diverse datasets containing high-dose-rate (HDR) brachytherapy treatment scans, leading to poor performance on images that include HDR implants.

Purpose: To implement and evaluate automatic organs-at-risk (OARs) segmentation models for use in prostatic-and-gynecological computed tomography (CT)-guided high-dose-rate brachytherapy treatment planning.

Methods and materials: 1316 computed tomography (CT) scans and corresponding segmentation files from 1105 prostatic-or-gynecological HDR patients treated at our institution from 2017 to 2024 were used for model training. Data sources comprised six CT scanners including a mobile CT unit with previously reported susceptibility to image streaking artifacts. Two UNet-derived model architectures, UNet++ and nnU-Net, were investigated for bladder and rectum model training. The models were tested on 100 CT scans and clinically-used segmentation files from 62 prostatic-or-gynecological HDR brachytherapy patients, disjoint from the training set, collected in 2024. Performance was evaluated using the Dice-Similarity-Coefficient (DSC) between model predicted contours and clinically-used contours on slices in common with the Clinical-Target-Volume (CTV). Additionally, a blinded evaluation of ten random test cases was conducted by three experienced planners.

Results: Median (interquartile range) 3D DSC on CTV-containing slices were 0.95 (0.04) and 0.87 (0.09) for the UNet++ bladder and rectum models, respectively, and 0.96 (0.03) and 0.88 (0.10) for the nnU-Net. The rank-sum test did not reveal statistically significant differences in these DSC (p = 0.15 and 0.27, respectively). The blinded evaluation scored trained models higher than clinically-used contours.

Conclusion: Both UNet-derived architectures perform similarly on the bladder and rectum and are adequately accurate to reduce contouring time in a review-and-edit context during HDR brachytherapy planning. The UNet++ models were chosen for implementation at our institution due to lower computing hardware requirements and are in routine clinical use.