Deep learning models for separate segmentations of intracerebral and intraventricular hemorrhage on head CT and segmentation quality assessment.

Journal: Medical physics
PMID: 39133935

Abstract

BACKGROUND: The volume measurement of intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) provides critical information for precise treatment of patients with spontaneous ICH but remains a big challenge, especially for IVH segmentation. However, the previously proposed ICH and IVH segmentation tools lack external validation and segmentation quality assessment.

Authors

  • Yifan Li
    College of Pharmacy, University of Minnesota, Minneapolis, Minnesota, USA.
  • Ruijie Zhang
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Ying Li
    School of Information Engineering, Chang'an University, Xi'an 710010, China.
  • Xinbing Zuo
    Department of Neurology, Qilu Hospital of Shandong University Dezhou Hospital, Shandong, China.
  • Qian Wang
    Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Shicai Zhang
    Department of Radiology, Qilu Hospital of Shandong University Dezhou Hospital, Shandong, China.
  • Xiankai Huo
    Department of Radiology, Qilu Hospital of Shandong University Dezhou Hospital, Shandong, China.
  • Zhenhe Liu
    Department of Radiology, Qilu Hospital of Shandong University Dezhou Hospital, Shandong, China.
  • Quan Zhang
    Department of Pulmonary and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
  • Meng Liang
    Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, People's Republic of China.

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