  Accurate organ at risk (OAR) segmentation is critical to reduce the
radiotherapy post-treatment complications. Consensus guidelines recommend a set
of more than 40 OARs in the head and neck (H&N) region, however, due to the
predictable prohibitive labor-cost of this task, most institutions choose a
substantially simplified protocol by delineating a smaller subset of OARs and
neglecting the dose distributions associated with other OARs. In this work we
propose a novel, automated and highly effective stratified OAR segmentation
(SOARS) system using deep learning to precisely delineate a comprehensive set
of 42 H&N OARs. SOARS stratifies 42 OARs into anchor, mid-level, and small &
hard subcategories, with specifically derived neural network architectures for
each category by neural architecture search (NAS) principles. We built SOARS
models using 176 training patients in an internal institution and independently
evaluated on 1327 external patients across six different institutions. It
consistently outperformed other state-of-the-art methods by at least 3-5% in
Dice score for each institutional evaluation (up to 36% relative error
reduction in other metrics). More importantly, extensive multi-user studies
evidently demonstrated that 98% of the SOARS predictions need only very minor
or no revisions for direct clinical acceptance (saving 90% radiation
oncologists workload), and their segmentation and dosimetric accuracy are
within or smaller than the inter-user variation. These findings confirmed the
strong clinical applicability of SOARS for the OAR delineation process in H&N
cancer radiotherapy workflows, with improved efficiency, comprehensiveness, and
quality.
