Learning anatomy from unlabelled CT volumes: A self‐supervised framework for improving prostate radiotherapy segmentation

Self-supervised pretraining via slice prediction enables anatomically informed feature learning and improves segmentation robustness under limited data conditions. This strategy enhances accuracy without reliance on manual labels during pretraining and is compatible with computationally lightweight architectures, making it well-suited in resource-constrained clinical environments.