Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.

Journal: BMC neurology
PMID: 39754084

Abstract

BACKGROUND AND PURPOSE: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a 3D convolutional neural network and a 3D Transformer-based model for white matter hyperintensities segmentation, focusing on their efficacy with limited datasets and similar computational resources.

Authors

  • Yun-Ting Chen
    Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123 Ta-Pei Road, Niao-Sung Dist, Kaohsiung City, 83305, Taiwan.
  • Yan-Cheng Huang
    Taiwan AI Labs, 6F., No. 70, Sec. 1, Chengde Rd., Datong Dist, 103622, Taipei City, Taiwan.
  • Hsiu-Ling Chen
    Department of Food Safety/Hygiene and Risk Management, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
  • Hsin-Chih Lo
    Taiwan AI Labs, 6F., No. 70, Sec. 1, Chengde Rd., Datong Dist, 103622, Taipei City, Taiwan.
  • Pei-Chin Chen
    Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan.
  • Chiun-Chieh Yu
    Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123 Ta-Pei Road, Niao-Sung Dist, Kaohsiung City, 83305, Taiwan.
  • Yi-Chin Tu
    Taiwan AI Labs, 6F., No. 70, Sec. 1, Chengde Rd., Datong Dist, 103622, Taipei City, Taiwan.
  • Tyng-Luh Liu
    Taiwan AI Labs, Taipei, Taiwan.
  • Wei-Che Lin
    Department of Radiology, Chang Gung Memorial Hospital, Kaohsiung Medical Centre, Kaohsiung, Taiwan.

Keywords

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