Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.

Journal: Medical physics

Published Date: Oct 28, 2017

Abstract

PURPOSE: Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DDCNN)-based method for fast and consistent auto-segmentation of these structures.

Authors

Kuo Men

State Key Laboratory of Advanced Materials for Smart Sensing, GRINM Group Co., Ltd., Beijing 100088, China.
Jianrong Dai

National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yexiong Li

Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.

Keywords

Automation Humans Image Processing, Computer-Assisted Machine Learning Neural Networks, Computer Organs at Risk Radiotherapy Planning, Computer-Assisted Rectal Neoplasms Time Factors Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (28963779)

Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.

Abstract

Authors

Keywords

External Resources

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