  Radiotherapy is a treatment where radiation is used to eliminate cancer
cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy
treatment planning to avoid damage to healthy organs. For nasopharyngeal
cancer, more than 20 OARs are needed to be precisely segmented in advance. The
challenge of this task lies in complex anatomical structure, low-contrast organ
contours, and the extremely imbalanced size between large and small organs.
Common segmentation methods that treat them equally would generally lead to
inaccurate small-organ labeling. We propose a novel two-stage deep neural
network, FocusNetv2, to solve this challenging problem by automatically
locating, ROI-pooling, and segmenting small organs with specifically designed
small-organ localization and segmentation sub-networks while maintaining the
accuracy of large organ segmentation. In addition to our original FocusNet, we
employ a novel adversarial shape constraint on small organs to ensure the
consistency between estimated small-organ shapes and organ shape prior
knowledge. Our proposed framework is extensively tested on both self-collected
dataset of 1,164 CT scans and the MICCAI Head and Neck Auto Segmentation
Challenge 2015 dataset, which shows superior performance compared with
state-of-the-art head and neck OAR segmentation methods.
