Evaluating deep learning-based image segmentation for radiotherapy planning in pelvic and abdominal cancers

In this study, we propose an attention-enhanced domain-adaptive segmentation framework tailored for radiotherapy planning in complex anatomical regions. By incorporating a context-aware attention mechanism and a fine-tuned adaptation module, our method aims to achieve high segmentation accuracy while maintaining computational efficiency. This framework not only improves performance on heterogeneous data but also facilitates robust and reproducible contouring of organs and lesions, contributing to more effective and individualized radiation therapy planning.