Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators

Abstract

Purpose: To evaluate the clinical applicability of a commercial artificial intelligence (AI)-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone-beam CT (iCBCT) acquisitions for intact prostate and prostate bed treatments. Methods and MaterialsDLAS models were trained using 116 iCBCT datasets with manually delineated organs-at-risk (OARs – bladder, femoral heads, and rectum) and target volumes (intact prostate and prostate bed) adhering to institution-specific contouring guidelines. An additional 25 intact prostate and prostate bed iCBCT datasets were utilized for model testing. Segmentation accuracy relative to a reference structure set was quantified using various geometric comparison metrics and qualitatively evaluated by trained physicists and physicians. These results were compared to those obtained for an additional DLAS-based model trained on planning CT (pCT) datasets and for a deformable image registration (DIR)-based automatic contour propagation method. Results: In most instances, statistically significant differences in the Dice similarity coefficient (DSC), 95% directed Hausdorff distance, and mean surface distance metrics were observed between the models, as the iCBCT-trained DLAS model outperformed the pCT-trained DLAS model and DIR-based method for all OARs and the intact prostate target volume. Mean DSC values for the proposed method were ≥0.90 for these volumes-of-interest. The iCBCT-trained DLAS model demonstrated a relatively suboptimal performance for the prostate bed segmentation, as the mean DSC value was <0.75 for this target contour. Overall, 90% of bladder, 93% of femoral head, 67% of rectum, and 92% of intact prostate contours generated by the proposed method were deemed clinically acceptable based on qualitative scoring, while approximately 63% of prostate bed contours required moderate or major manual editing to adhere to institutional contouring guidelines. Conclusion: The proposed method presents the potential for improved segmentation accuracy and efficiency when compared to the DIR-based automatic contour propagation method as commonly applied in CBCT-based dose evaluation and calculation studies.