Automatic Primary Gross Tumor Volume Segmentation for Nasopharyngeal Carcinoma using ResSE-UNet.

Nasopharyngeal carcinoma (NPC) is an endemic disease within specific regions in the world. Radiotherapy is the standard treatment for NPC and accurate segmentation of primary gross tumor volume (GTV) is a critical process of continue therapy. In this paper we proposed a ResSE-UNet network and a Ternary Cross-Entropy (TCE) loss function for delineation of GTV. ResSE-UNet employed ResSE blocks to replace convolutional blocks in the original UNet to extract better features, and reduced the number of down-sampling processing to keep relatively high resolution of the images. TCE combined dice loss and Binary cross-entropy loss for larger gradient and better stability in training. The experimental results showed that among all combinations of networks and loss functions, the ResSE-UNet with TCE loss achieved the best segmentation performance, i.e. about 0.84 DSC can be obtained.