Deep learning-based fine-grained assessment of aneurysm wall characteristics using 4D-CT angiography.

Journal: PeerJ
PMID:

Abstract

PURPOSE: This study proposes a novel deep learning-based approach for aneurysm wall characteristics, including thin-walled (TW) and hyperplastic-remodeling (HR) regions.

Authors

  • Teerawat Kumrai
    Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, Japan.
  • Takuya Maekawa
    Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, 565-0871, Japan. maekawa@ist.osaka-u.ac.jp.
  • Yixuan Chen
    Terry Fox Laboratory, BC Cancer Research, Vancouver, British Columbia, Canada.
  • Yoshie Sugiyama
    Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, Japan.
  • Masatoshi Takagaki
    Department of Neurosurgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
  • Shigeo Yamashiro
    Department of Neurosurgery, Saiseikai Kumamoto Hospital, Stroke Center.
  • Katsumi Takizawa
    Department of Neurosurgery, Japanese Red Cross Asahikawa Hospital, Asahikawa, Hokkaido, Japan.
  • Tsutomu Ichinose
    From the Departments of Diagnostic and Interventional Radiology (D.U., A.Y., S.L.W., H. Tatekawa, H. Takita, T.H., A.S., Y.M.), Neurosurgery (T. Ichinose, H.A., Y.W., T.G.), and Medical Statistics (D.K.), Graduate School of Medicine, Osaka City University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; and Department of Radiology, Osaka City University Hospital, 1-5-7 Asahi-machi, Abeno-ku, Osaka, 545-8586, Japan (Y.K., T. Ichida).
  • Fujimaro Ishida
    Department of Neurosurgery, Mie Chuo Medical Center, 2158-5 Myojin-cho, Hisai, Tsu, Mie, 514-1101, Japan.
  • Haruhiko Kishima
    Department of Neurosurgery, Osaka University Graduate School of Medicine.