Deep Learning Models for CT Segmentation of Invasive Pulmonary Aspergillosis, Mucormycosis, Bacterial Pneumonia and Tuberculosis: A Multicentre Study.

Journal: Mycoses
Published Date: Jul 1, 2025

Abstract

BACKGROUND: The differential diagnosis of invasive pulmonary aspergillosis (IPA), pulmonary mucormycosis (PM), bacterial pneumonia (BP) and pulmonary tuberculosis (PTB) are challenging due to overlapping clinical and imaging features. Manual CT lesion segmentation is time-consuming, deep-learning (DL)-based segmentation models offer a promising solution, yet disease-specific models for these infections remain underexplored.

Authors

  • Yun Li
    School of Public Health, University of Michigan, Ann Arbor, MI, USA.
  • Feifei Huang
    School of Nursing, Fujian Medical University, Fuzhou, 350122, Fujian, China.
  • Deyan Chen
    Shenyang Neusoft Intelligent Medical Technology Research Institute Co., Ltd, Shenyang, China.
  • Youwen Zhang
    HanZhong Central Hospital of Shaanxi Province, HanZhong, Shaanxi 723100, China.
  • Xia Zhang
    School of Computer Science, Engineering Northeastern University, No.195 Chuangxin Road Hunnan District, Shenyang 110169, China.
  • Lina Liang
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Junnan Pan
    Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, China.
  • Lunfang Tan
    National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Shuyi Liu
    The Experimental High School Attached to Beijing Normal University, No. 14 Erlong Road, Beijing 100051, PR China.
  • Junfeng Lin
    National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Zhengtu Li
    National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Guodong Hu
    Shandong Key Laboratory of Biophysics, Dezhou University, Dezhou 253023, China.
  • Huai Chen
    Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China.
  • Chengbao Peng
    Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, 110169, China. pengcb@neusoft.com.
  • Feng Ye
    State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, National Center for Respiratory Medicine, Guangzhou, China.
  • Jinping Zheng
    National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.

Keywords

External Resources

Popular Topics
Recent Journals