no code implementations • 29 Feb 2024 • Jiaqi Cui, Yuanyuan Xu, Jianghong Xiao, Yuchen Fei, Jiliu Zhou, Xingcheng Peng, Yan Wang
Deep learning has facilitated the automation of radiotherapy by predicting accurate dose distribution maps.
no code implementations • 6 Nov 2023 • Zhenghao Feng, Lu Wen, Jianghong Xiao, Yuanyuan Xu, Xi Wu, Jiliu Zhou, Xingchen Peng, Yan Wang
In the forward process, DiffDose transforms dose distribution maps into pure Gaussian noise by gradually adding small noise and a noise predictor is simultaneously trained to estimate the noise added at each timestep.
no code implementations • 20 Aug 2023 • Jie Zeng, Zeyu Han, Xingchen Peng, Jianghong Xiao, Peng Wang, Yan Wang
Recently, deep learning (DL) has automated and accelerated the clinical radiation therapy (RT) planning significantly by predicting accurate dose maps.
3 code implementations • 3 Nov 2021 • Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, Shaoting Zhang
Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation.
no code implementations • 11 Jun 2020 • Pin Tang, Chen Zu, Mei Hong, Rui Yan, Xingchen Peng, Jianghong Xiao, Xi Wu, Jiliu Zhou, Luping Zhou, Yan Wang
In this paper, we propose a Dense SegU-net (DSU-net) framework for automatic NPC segmentation in MRI.