Comparative assessment of established and deep learning-based segmentation methods for hippocampal volume estimation in brain magnetic resonance imaging analysis.

Journal: NMR in biomedicine
PMID: 38712667

Abstract

In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established techniques, FreeSurfer-Aseg and FSL-FIRST, using three-dimensional T1-weighted MRI scans (n = 1447) procured from public databases. Our evaluation focused on the accuracy and reproducibility of these tools in estimating hippocampal volume. The findings suggest that both SynthSeg and TigerBx are on a par with Aseg and FIRST in terms of segmentation accuracy and reproducibility, but offer a significant advantage in processing speed, generating results in less than 1 min compared with several minutes to hours for the latter tools. In terms of Alzheimer's disease classification based on the hippocampal atrophy rate, SynthSeg and TigerBx exhibited superior performance. In conclusion, we evaluated the capabilities of two deep learning-based segmentation techniques. The results underscore their potential value in clinical and research environments, particularly when investigating neurological conditions associated with hippocampal structures.

Authors

  • Hsi-Chun Wang
    Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
  • Chia-Sho Chen
    Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
  • Chung-Chin Kuo
    Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
  • Teng-Yi Huang
    Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan.
  • Kuei-Hong Kuo
    Division of Medical Image, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
  • Tzu-Chao Chuang
    Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan.
  • Yi-Ru Lin
    Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
  • Hsiao-Wen Chung
    Graduate Institute of Biomedical Electrics and Bioinformatics, National Taiwan University, Taipei, Taiwan.

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