Automated Detection of Spinal Schwannomas Utilizing Deep Learning Based on Object Detection From Magnetic Resonance Imaging.

Abstract

Authors

• Sadayuki Ito
  Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan.
• Kei Ando
  Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan.
• Kazuyoshi Kobayashi
  Nagoya University Graduate School of Medicine, Nagoya, Japan.
• Hiroaki Nakashima
  Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan.
• Masahiro Oda
  Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
• Masaaki Machino
  Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan.
• Shunsuke Kanbara
  Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan.
• Taro Inoue
  Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan.
• Hidetoshi Yamaguchi
  Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan.
• Hiroyuki Koshimizu
  Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan.
• Kensaku Mori
  Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
• Naoki Ishiguro
  Nagoya University Graduate School of Medicine, Nagoya, Japan.
• Shiro Imagama
  Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan.

Keywords

External Resources