A general model for head and neck auto‐segmentation with patient pre‐treatment imaging during adaptive radiation therapy

The inclusion of patient pre-treatment images and segmentation labels can improve auto-segmentation performance during HN ART, in particular for structures with high variability or low contrast. Including pre-treatment data at DL model inference time (GAM) may give improvements over standard DL models for the GTVp and several OARs, while eliminating the need for costly model retraining with new patient cohorts. However, rigid registration provides similar performance to adaptive DL models for the GTVp and most OARs.