Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images.

Journal: AJNR. American journal of neuroradiology
Published Date: Nov 5, 2020

Abstract

BACKGROUND AND PURPOSE: Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. Our aim was to develop and validate a deep learning-based automatic brain segmentation and classification algorithm for the diagnosis of Alzheimer disease using 3D T1-weighted brain MR images.

Authors

  • C H Suh
    From the Department of Radiology and Research Institute of Radiology (C.H.S., W.H.S., S.J.K.).
  • W H Shim
    From the Department of Radiology and Research Institute of Radiology (C.H.S., W.H.S., S.J.K.).
  • S J Kim
    From the Department of Radiology and Research Institute of Radiology (C.H.S., W.H.S., S.J.K.) sjkimjb5@gmail.com.
  • J H Roh
    Department of Neurology (J.H.R., J.-H.L.).
  • J-H Lee
    Department of Neurology (J.H.R., J.-H.L.).
  • M-J Kim
    Health Screening and Promotion Center (M.-J.K.), Asan Medical Center, Seoul, Republic of Korea.
  • S Park
  • W Jung
    VUNO Inc (S.P., W.J., J.S.), Seoul, Republic of Korea.
  • J Sung
    VUNO Inc (S.P., W.J., J.S.), Seoul, Republic of Korea.
  • G-H Jahng
    Department of Radiology (G.-H.J.), Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea.

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