Automatic Segmentation and Radiomics for Identification and Activity Assessment of CTE Lesions in Crohn's Disease.

Journal: Inflammatory bowel diseases
Published Date: Nov 4, 2024

Abstract

BACKGROUND: The purpose of this article is to develop a deep learning automatic segmentation model for the segmentation of Crohn's disease (CD) lesions in computed tomography enterography (CTE) images. Additionally, the radiomics features extracted from the segmented CD lesions will be analyzed and multiple machine learning classifiers will be built to distinguish CD activity.

Authors

  • Yankun Gao
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
  • Bo Zhang
    Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China.
  • Dehan Zhao
    Department of Precision Machinery and Precision Instruments, University of Science and Technology of China, Hefei, China.
  • Shuai Li
    School of Molecular Biosciences, Center for Reproductive Biology, College of Veterinary Medicine, Washington State University.
  • Chang Rong
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China.
  • Mingzhai Sun
    University of Science and Technology of China, Department of Precision Machinery and Precision Instrumentation, Hefei, China.
  • Xingwang Wu
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

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