@inproceedings{28dbfa1cc4454ca3b6554f1660913611,
title = "Automatic primary gross tumor volume segmentation for nasopharyngeal carcinoma using ResSE-UNet",
abstract = "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.",
keywords = "GTV segmentation, Nasopharyngeal carcinoma, ResSE-UNet, Ternary cross-entropy",
author = "Zhihao Jin and Li, {Xue Chen} and Linlin Shen and Jinyi Lang and Jie Li and Junxiang Wu and Peng Xu and Jiang Duan",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 ; Conference date: 28-07-2020 Through 30-07-2020",
year = "2020",
month = jul,
doi = "10.1109/CBMS49503.2020.00116",
language = "English",
series = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "585--590",
editor = "{de Herrera}, {Alba Garcia Seco} and {Rodriguez Gonzalez}, Alejandro and KC Santosh and Zelalem Temesgen and Bridget Kane and Paolo Soda",
booktitle = "Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020",
address = "United States",
}
