Z
Zhao Peng
Researcher at University of Science and Technology of China
Publications - 13
Citations - 183
Zhao Peng is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Monte Carlo method & Computer science. The author has an hindex of 6, co-authored 11 publications receiving 92 citations.
Papers
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Journal ArticleDOI
A Method of Rapid Quantification of Patient-Specific Organ Dose for CT Using Coupled Deep-Learning based Multi-Organ Segmentation and GPU-accelerated Monte Carlo Dose Computing
Zhao Peng,Xi Fang,Pingkun Yan,Hongming Shan,Tianyu Liu,Xi Pei,Ge Wang,Bob Liu,Mannudeep K. Kalra,X. George Xu +9 more
TL;DR: This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of multiple radiosensitive organs from CT images with the GPU-based Monte Carlo rapid organ dose calculation and achieves smaller absolute RDEs for all organs.
Journal ArticleDOI
Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients.
TL;DR: The auto‐segmentation model was as accurate as the medical resident but with much better efficiency in this study and offers additional perceivable advantages of being consistent and ever improving when compared with manual approaches.
Journal ArticleDOI
A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing
Zhao Peng,Xi Fang,Pingkun Yan,Hongming Shan,Tianyu Liu,Xi Pei,Ge Wang,Bob Liu,Mannudeep K. Kalra,X. George Xu +9 more
TL;DR: In this paper, a deep convolutional neural network (CNN) based on the U-Net for organ segmentation is developed and trained to automatically delineate multiple radiosensitive organs from CT images.
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A method of using deep learning to predict three-dimensional dose distributions for intensity-modulated radiotherapy of rectal cancer.
Jieping Zhou,Zhao Peng,Yuchen Song,Yankui Chang,Xi Pei,Liusi Sheng,X. George Xu,X. George Xu +7 more
TL;DR: This study developed a more general deep learning model by considering beam configurations input and achieved an accurate 3D voxel‐wise dose prediction for rectal cancer treated by IMRT, a potentially easier clinical implementation for more comprehensive automatic planning.
Journal ArticleDOI
MCDNet – A Denoising Convolutional Neural Network to Accelerate Monte Carlo Radiation Transport Simulations: A Proof of Principle With Patient Dose From X-Ray CT Imaging
TL;DR: This paper proposes and demonstrates the Monte Carlo Denoising Net (MCDNet), a convolutional encoder-decoder neural network, for the purpose of accelerating the MC radiation transport simulations for patient CT dosimetry, and is the first CNN-based method to speed-up MC radiation Transport simulations involving 3D and heterogeneous patient anatomies for x-ray CT.