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

Abstract

Background: During head and neck (HN) radiation therapy, patients may undergo anatomical changes due to tumor shrinkage or weight loss. For these patients, adaptive radiation therapy (ART) is required to correct treatment plans and to ensure that the prescribed radiation dose is delivered to the tumor while minimizing dose to the surrounding organs-at-risk (OARs). Patient pre-treatment images and segmentation labels are always available during ART and may be incorporated into deep learning (DL) auto-segmentation models to improve performance on mid-treatment images. Purpose: Existing DL methods typically incorporate pre-treatment data during training. In this work, we investigated whether in..