Automatic collateral quantification in acute ischemic stroke using U-net.

Journal: Frontiers in neurology
Published Date: Jan 1, 2025

Abstract

OBJECTIVES: To harness the U-Net deep learning framework for automated quantification of collateral circulation in acute ischemic stroke (AIS) via computed tomography angiography (CTA) images, comparing its performance against traditional visual collateral scores (vCS).

Authors

  • Qingqing Lu
    Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China.
  • Hongyi Chen
    Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China.
  • Junyan Fu
    Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China. Electronic address: 22111220057@m.fudan.edu.cn.
  • Xiaodong Zheng
    Department of Obstetrics and Gynecology, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People's Hospital, Wenzhou, 325000, China. Electronic address: 413479446@qq.com.
  • Yiren Xu
    Department of Radiology, The First Affiliated Hospital of Ningbo University, Ningbo University, Ningbo, China.
  • Yuning Pan
    Department of Radiology, Ningbo First Hospital, Ningbo, Zhejiang, China.

Keywords

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