Comprehensive Head and Neck Organs at Risk Segmentation Using Stratified Learning and Neural Architecture Search.

PURPOSE/OBJECTIVE(S) Organs at risk (OAR) segmentation is an essential step in the radiotherapy of head and neck (HN and on S&H OAR of 10.1% DSC increase, 1.0mm ASD reduction, respectively. Using the NPC patients as an unseen testing set, our method has achieved an average DSC of 76.3% and 1.3mm ASD, which is consistent as in the OPX dataset. This result demonstrates the robustness and generalizability of our method in patients, even with various cancer types. CONCLUSION We introduced a new stratified method for segmenting a large comprehensive set of H&N OARs. Our method integrates multi-stage segmentation and NAS in a synergy for the first time. It was trained using OPX patients and achieved state-of-the-art performance and generalized well to patients of NPC. Our method is a critical step towards an automated, accurate, and dependable OAR segmentation system in various H&N cancers.