  OAR segmentation is a critical step in radiotherapy of head and neck (H&N)
cancer, where inconsistencies across radiation oncologists and prohibitive
labor costs motivate automated approaches. However, leading methods using
standard fully convolutional network workflows that are challenged when the
number of OARs becomes large, e.g. > 40. For such scenarios, insights can be
gained from the stratification approaches seen in manual clinical OAR
delineation. This is the goal of our work, where we introduce stratified organ
at risk segmentation (SOARS), an approach that stratifies OARs into anchor,
mid-level, and small & hard (S&H) categories. SOARS stratifies across two
dimensions. The first dimension is that distinct processing pipelines are used
for each OAR category. In particular, inspired by clinical practices, anchor
OARs are used to guide the mid-level and S&H categories. The second dimension
is that distinct network architectures are used to manage the significant
contrast, size, and anatomy variations between different OARs. We use
differentiable neural architecture search (NAS), allowing the network to choose
among 2D, 3D or Pseudo-3D convolutions. Extensive 4-fold cross-validation on
142 H&N cancer patients with 42 manually labeled OARs, the most comprehensive
OAR dataset to date, demonstrates that both pipeline- and NAS-stratification
significantly improves quantitative performance over the state-of-the-art (from
69.52% to 73.68% in absolute Dice scores). Thus, SOARS provides a powerful and
principled means to manage the highly complex segmentation space of OARs.
