FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD.

Journal: BMC medical imaging
Published Date: Jul 10, 2025

Abstract

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a significant risk factor for liver cancer and cardiovascular diseases, imposing substantial social and economic burdens. Computed tomography (CT) scans are crucial for diagnosing NAFLD and assessing its severity. However, current manual measurement techniques require considerable human effort and resources from radiologists, and there is a lack of standardized methods for classifying the severity of NAFLD in existing research.

Authors

  • Liting Fan
  • Yi Lei
    Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen, 518035, China.
  • Feng Song
    Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China.
  • Xiangfei Sun
    Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou 511443, China.
  • Zhuowei Zhang
    College of Medical Imaging, Shanxi Medical University, Xinjian South Road, Taiyuan, Shanxi, 030001, China. zhangzhuowei312@126.com.

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