Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement.

Journal: BMC medical imaging
PMID: 34388981

Abstract

BACKGROUND: Accurate measurement of hemorrhage volume is critical for both the prediction of prognosis and the selection of appropriate clinical treatment after spontaneous intracerebral hemorrhage (ICH). This study aimed to evaluate the performance and accuracy of a deep learning-based automated segmentation algorithm in segmenting spontaneous intracerebral hemorrhage (ICH) volume either with or without intraventricular hemorrhage (IVH) extension. We compared this automated pipeline with two manual segmentation techniques.

Authors

  • Tao Wang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Na Song
    The Department of Radiology, The General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
  • Lingling Liu
    The Department of Radiology, The General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
  • Zichao Zhu
    The Department of Radiology, The General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
  • Bing Chen
    Department of Critical Care Medicine, The Second Hospital of Tianjin Medical University, Tianjin, China.
  • Wenjun Yang
    Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
  • Zhiqiang Chen
    Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.

Keywords

External Resources

Popular Topics
Recent Journals