Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans.

Journal: IEEE journal of translational engineering in health and medicine
Published Date: May 9, 2024

Abstract

OBJECTIVE: Pulmonary cavity lesion is one of the commonly seen lesions in lung caused by a variety of malignant and non-malignant diseases. Diagnosis of a cavity lesion is commonly based on accurate recognition of the typical morphological characteristics. A deep learning-based model to automatically detect, segment, and quantify the region of cavity lesion on CT scans has potential in clinical diagnosis, monitoring, and treatment efficacy assessment.

Authors

  • Wenyu Xing
    Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China; Human Phenome Institute, Fudan University, Shanghai 200438, China.
  • Yanping Yang
    Shanghai Institute of Infectious Disease and Biosecurity, Fudan University Shanghai 200032 China.
  • Yanan Zhou
    Shanghai Institute of Infectious Disease and Biosecurity, Fudan University Shanghai 200032 China.
  • Tao Jiang
    Department of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
  • Yifang Li
  • Yuanlin Song
    Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China; Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, 200032, China.
  • Dongni Hou
    Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China; Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, 200032, China. Electronic address: hou.dongni@zs-hospital.sh.cn.
  • Dean Ta

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