Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset.

Journal: Biomedical engineering online
Published Date: Jan 3, 2019

Abstract

BACKGROUND: Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung parenchyma with commonalities on CT images across subjects, diseases and CT scanners, and lung lesions presenting various appearances. Segmentation of lung parenchyma can help locate and analyze the neighboring lesions, but is not well studied in the framework of machine learning.

Authors

  • Mingjie Xu
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No. 195 Chuangxin Avenue, Hunnan District, Shenyang, 110169, China.
  • Shouliang Qi
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Life Science Building, 500 Zhihui Street, Hun'nan District, Shenyang, 110169, China. qisl@bmie.neu.edu.cn.
  • Yong Yue
    Department of Radiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Shenyang, 110004, China.
  • Yueyang Teng
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Life Science Building, 500 Zhihui Street, Hun'nan District, Shenyang, 110169, China.
  • Lisheng Xu
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
  • Yudong Yao
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China.
  • Wei Qian
    Department of Electrical and Computer Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No.11, Lane 3, Wenhua Road, Heping District, Shenyang, Liaoning 110819, China. Electronic address: wqian@utep.edu.

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