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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 ofradiation therapy for NPC treatment. Despite the stateof-the-art performanceachieved by Convolutional Neural Networks (CNNs) for automatic segmentation ofOARs, existing methods do not provide uncertainty estimation of thesegmentation results for treatment planning, and their accuracy is stilllimited by several factors, including the low contrast of soft tissues in CT,highly imbalanced sizes of OARs and large inter-slice spacing. To address theseproblems, we propose a novel framework for accurate OAR segmentation withreliable uncertainty estimation. First, we propose a Segmental Linear Function(SLF) to transform the intensity of CT images to make multiple organs moredistinguishable than existing methods based on a simple window width/level thatoften 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 scanswith anisotropic spacing. Thirdly, existing hardness-aware loss function oftendeal with class-level hardness, but our proposed attention to hard voxels (ATH)uses a voxel-level hardness strategy, which is more suitable to dealing withsome hard regions despite that its corresponding class may be easy. Our code isnow available at https://github.com/HiLab-git/SepNet.

Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss