The following publications are possibly variants of this publication:
- Automatic segmentation of organs-at-risk from head-and-neck CT using separable convolutional neural network with hard-region-weighted lossWenhui Lei, Haochen Mei, Zhengwentai Sun, Shan Ye, Ran Gu, Huan Wang, Rui Huang, Shichuan Zhang, Shaoting Zhang, Guotai Wang. ijon, 442:184-199, 2021. [doi]
- Tubular Structure-Aware Convolutional Neural Networks for Organ at Risks Segmentation in Cervical Cancer RadiotherapyXinran Wu, Ming Cui, Yuhua Gao, Deyu Sun, He Ma, Erlei Zhang, Yaoqin Xie, Nazar Zaki, Wenjian Qin. miccai 2022: 131-140 [doi]
- 3D Lightweight Network for Simultaneous Registration and Segmentation of Organs-at-Risk in CT Images of Head and Neck CancerBin Huang, Yufeng Ye, Ziyue Xu, Zongyou Cai, Yan He, Zhangnan Zhong, LingXiang Liu, Xin Chen 0025, Hanwei Chen, Bingsheng Huang. tmi, 41(4):951-964, 2022. [doi]
- Automatic segmentation of the clinical target volume and organs at risk for rectal cancer radiotherapy using structure-contextual representations based on 3D high-resolution networkYiwei Yang, Rui Huang, Guofeng Lv, Zhiqiang Hu, Guoping Shan, Jie Zhang, Xue Bai, Peng Liu, Hongsheng Li 0001, Ming Chen. bspc, 73:103362, 2022. [doi]