  In this paper, we propose an end-to-end deep neural network for solving the
problem of imbalanced large and small organ segmentation in head and neck (HaN)
CT images. To conduct radiotherapy planning for nasopharyngeal cancer, more
than 10 organs-at-risk (normal organs) need to be precisely segmented in
advance. However, the size ratio between large and small organs in the head
could reach hundreds. Directly using such imbalanced organ annotations to train
deep neural networks generally leads to inaccurate small-organ label maps. We
propose a novel end-to-end deep neural network to solve this challenging
problem by automatically locating, ROI-pooling, and segmenting small organs
with specifically designed small-organ sub-networks while maintaining the
accuracy of large organ segmentation. A strong main network with densely
connected atrous spatial pyramid pooling and squeeze-and-excitation modules is
used for segmenting large organs, where large organs' label maps are directly
output. For small organs, their probabilistic locations instead of label maps
are estimated by the main network. High-resolution and multi-scale feature
volumes for each small organ are ROI-pooled according to their locations and
are fed into small-organ networks for accurate segmenting small organs. Our
proposed network is extensively tested on both collected real data and the
\emph{MICCAI Head and Neck Auto Segmentation Challenge 2015} dataset, and shows
superior performance compared with state-of-the-art segmentation methods.
