A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer.

Journal: Journal of applied clinical medical physics
PMID: 30418701

Abstract

PURPOSE: Convolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning (prone or supine) for radiotherapy. In this study, we investigated the effect of position orientation on segmentation using CNN.

Authors

  • Kuo Men
    State Key Laboratory of Advanced Materials for Smart Sensing, GRINM Group Co., Ltd., Beijing 100088, China.
  • Pamela Boimel
  • James Janopaul-Naylor
  • Chingyun Cheng
  • Haoyu Zhong
  • Mi Huang
  • Huaizhi Geng
  • Yong Fan
    CPB/ECMO Children's Hospital, Zhejiang University School of Medicine, 310052 Hangzhou, Zhejiang, China.
  • John P Plastaras
  • Edgar Ben-Josef
    Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Ying Xiao
    Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.

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