Deep Learning-based Hierarchical Brain Segmentation with Preliminary Analysis of the Repeatability and Reproducibility.

Journal: Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Published Date: Jul 2, 2024

Abstract

PURPOSE: We developed new deep learning-based hierarchical brain segmentation (DLHBS) method that can segment T1-weighted MR images (T1WI) into 107 brain subregions and calculate the volume of each subregion. This study aimed to evaluate the repeatability and reproducibility of volume estimation using DLHBS and compare them with those of representative brain segmentation tools such as statistical parametric mapping (SPM) and FreeSurfer (FS).

Authors

  • Masami Goto
    Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan.
  • Koji Kamagata
  • Christina Andica
    Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
  • Kaito Takabayashi
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
  • Wataru Uchida
    Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
  • Tsubasa Goto
    Medical System Research & Development Center, FUJIFILM Corporation, Tokyo, Japan.
  • Takuya Yuzawa
    Medical System Research & Development Center, FUJIFILM Corporation, Tokyo, Japan.
  • Yoshiro Kitamura
    Imaging Technology Center, Fujifilm Corporation, Tokyo, Japan.
  • Taku Hatano
    Department of Neurology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
  • Nobutaka Hattori
    Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan.
  • Shigeki Aoki
  • Hajime Sakamoto
    Department of Radiology Technology, Juntendo University Faculty of Health Science, Tokyo, Japan.
  • Yasuaki Sakano
    Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan.
  • Shinsuke Kyogoku
    Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan.
  • Hiroyuki Daida
    Department of Radiology Technology, Juntendo University Faculty of Health Science, Tokyo, Japan.

Keywords

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