Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study

no code implementations • 1 Nov 2021 • Dazhou Guo, Jia Ge, Xianghua Ye, Senxiang Yan, Yi Xin, Yuchen Song, Bing-shen Huang, Tsung-Min Hung, Zhuotun Zhu, Ling Peng, Yanping Ren, Rui Liu, Gong Zhang, Mengyuan Mao, Xiaohua Chen, Zhongjie Lu, Wenxiang Li, Yuzhen Chen, Lingyun Huang, Jing Xiao, Adam P. Harrison, Le Lu, Chien-Yu Lin, Dakai Jin, Tsung-Ying Ho

Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications.

Neural Architecture Search Segmentation

Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT

no code implementations • 11 Oct 2021 • Xianghua Ye, Dazhou Guo, Chen-Kan Tseng, Jia Ge, Tsung-Min Hung, Ping-Ching Pai, Yanping Ren, Lu Zheng, Xinli Zhu, Ling Peng, Ying Chen, Xiaohua Chen, Chen-Yu Chou, Danni Chen, Jiaze Yu, Yuzhen Chen, Feiran Jiao, Yi Xin, Lingyun Huang, Guotong Xie, Jing Xiao, Le Lu, Senxiang Yan, Dakai Jin, Tsung-Ying Ho

252 institution-1 patients had a treatment planning-CT (pCT) and a pair of diagnostic FDG-PETCT; 354 patients from other 3 institutions had only pCT.