  Nasopharyngeal Carcinoma (NPC) is a leading form of Head-and-Neck (HAN)
cancer in the Arctic, China, Southeast Asia, and the Middle East/North Africa.
Accurate segmentation of Organs-at-Risk (OAR) from Computed Tomography (CT)
images with uncertainty information is critical for effective planning of
radiation therapy for NPC treatment. Despite the stateof-the-art performance
achieved by Convolutional Neural Networks (CNNs) for automatic segmentation of
OARs, existing methods do not provide uncertainty estimation of the
segmentation results for treatment planning, and their accuracy is still
limited by several factors, including the low contrast of soft tissues in CT,
highly imbalanced sizes of OARs and large inter-slice spacing. To address these
problems, we propose a novel framework for accurate OAR segmentation with
reliable uncertainty estimation. First, we propose a Segmental Linear Function
(SLF) to transform the intensity of CT images to make multiple organs more
distinguishable than existing methods based on a simple window width/level that
often gives a better visibility of one organ while hiding the others. Second,
to deal with the large inter-slice spacing, we introduce a novel 2.5D network
(named as 3D-SepNet) specially designed for dealing with clinic HAN CT scans
with anisotropic spacing. Thirdly, existing hardness-aware loss function often
deal with class-level hardness, but our proposed attention to hard voxels (ATH)
uses a voxel-level hardness strategy, which is more suitable to dealing with
some hard regions despite that its corresponding class may be easy. Our code is
now available at https://github.com/HiLab-git/SepNet.
