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OAR segmentation is a critical step in radiotherapy of head and neck (H&N)cancer, where inconsistencies across radiation oncologists and prohibitivelabor costs motivate automated approaches. However, leading methods usingstandard fully convolutional network workflows that are challenged when thenumber of OARs becomes large, e.g. > 40. For such scenarios, insights can begained from the stratification approaches seen in manual clinical OARdelineation. This is the goal of our work, where we introduce stratified organat risk segmentation (SOARS), an approach that stratifies OARs into anchor,mid-level, and small & hard (S&H) categories. SOARS stratifies across twodimensions. The first dimension is that distinct processing pipelines are usedfor each OAR category. In particular, inspired by clinical practices, anchorOARs are used to guide the mid-level and S&H categories. The second dimensionis that distinct network architectures are used to manage the significantcontrast, size, and anatomy variations between different OARs. We usedifferentiable neural architecture search (NAS), allowing the network to chooseamong 2D, 3D or Pseudo-3D convolutions. Extensive 4-fold cross-validation on142 H&N cancer patients with 42 manually labeled OARs, the most comprehensiveOAR dataset to date, demonstrates that both pipeline- and NAS-stratificationsignificantly improves quantitative performance over the state-of-the-art (from69.52% to 73.68% in absolute Dice scores). Thus, SOARS provides a powerful andprincipled means to manage the highly complex segmentation space of OARs.

Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search