Translating Simulated Images to Real Radiograph using Generative Adversarial Networks: Estimation of Pelvic Tilt from Real Images

• HIASA Yuta

  Division of Information Science, Nara Institute of Science and Technology

• OTAKE Yoshito

  Division of Information Science, Nara Institute of Science and Technology

• MATSUOKA Takumi

  Division of Information Science, Nara Institute of Science and Technology

• TAKAO Masaki

  Graduate School of Medicine, Osaka University

• SUGANO Nobuhiko

  Graduate School of Medicine, Osaka University

• SATO Yoshinobu

  Division of Information Science, Nara Institute of Science and Technology

Bibliographic Information

Other Title

• GANを用いた実X線画像からの疑似X線画像変換 ―骨盤傾斜角推定手法の実画像への適用―

Abstract

In total hip arthroplasty, pelvic tilt in standing position is important in preoperative planning of the optimum placement angle of the cup. However, such tilt angle cannot be accessed from CT images scanned in the supine position. Previous study has been focused on radiographs scanned in the standing position. 2D-3D registration between a radiograph and a patient-specific CT image achieved that, but its application was limited due to the radiation exposure at CT acquisition. To solve this problem, we have proposed a method to estimate pelvic tilt angle from only single radiograph using convolution neural networks and tested with simulated images. However, its application to real radiographs is difficult due to the influence of noises and the X-ray spectrum. In this paper, we introduce estimation of pelvic tilt from real radiographs using a generative adversarial networks translating a real radiograph to a simulated image.

Journal

• Medical Imaging Technology

  Medical Imaging Technology 37 (3), 125-129, 2019-05-25

  The Japanese Society of Medical Imaging Technology

Keywords

• Pelvic tilt
• Radiograph
• Image synthesis

Details

• CRID: 1390845713076448256
• NII Article ID: 130007662439
• DOI: 10.11409/mit.37.125
• ISSN: 21853193
• ISSN: 0288450X
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed