  Nasopharyngeal carcinoma (NPC) is a kind of malignant tumor. Accurate and
automatic segmentation of organs at risk (OAR) of computed tomography (CT)
images is clinically significant. In recent years, deep learning models
represented by U-Net have been widely applied in medical image segmentation
tasks, which can help doctors with reduction of workload and get accurate
results more quickly. In OAR segmentation of NPC, the sizes of OAR are
variable, especially, some of them are small. Traditional deep neural networks
underperform during segmentation due to the lack use of global and multi-size
information. This paper proposes a new SE-Connection Pyramid Network
(SECP-Net). SECP-Net extracts global and multi-size information flow with se
connection (SEC) modules and a pyramid structure of network for improving the
segmentation performance, especially that of small organs. SECP-Net also
designs an auto-context cascaded network to further improve the segmentation
performance. Comparative experiments are conducted between SECP-Net and other
recently methods on a dataset with CT images of head and neck. Five-fold cross
validation is used to evaluate the performance based on two metrics, i.e., Dice
and Jaccard similarity. Experimental results show that SECP-Net can achieve
SOTA performance in this challenging task.
