Multi-Organ Anatomical Context Improves Ureter Segmentation in Arterial-Phase CT: A Systematic Evaluation of nnU-Net Configurations

Accurate segmentation of the ureter on abdominal computed tomography (CT) remains challenging due to its thin tubular structure and limited expert-annotated training data. While recent deep learning approaches have shown promise on non-contrast CT, arterial-phase imaging remains under-researched. We systematically compared nnU-Net-based configurations for ureter segmentation on arterial-phase CT using 25 radiologist-annotated cases from Seoul St. Mary’s Hospital. Seven training strategies were evaluated with five-fold cross-validation: binary ureter-only segmentation, multi-organ training with anatomical context from eight structures, alternative encoder architectures (ResEncM), specialized loss functions (Tversky, clDice), and a multi-phase fusion architecture. Multi-organ training with Tversky-Focal loss (Config 6) achieved the highest mean Dice of 0.743 ± 0.021 with the best clDice connectivity score (0.800 ± 0.046) and lowest fragmentation (6.56 connected components). Multi-phase fusion yielded a mean Dice of 0.713 on the 12-case subset; a controlled arterial-phase single-channel ablation on the identical 12-case subset achieved 0.721, marginally exceeding the two-channel fusion result (0.713). These findings are scoped to a single-institution exploratory cohort and should be interpreted as internally comparative benchmarking results; they may not generalize to other centres, scanners, or patient populations, and do not constitute clinical validation.