Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network.

Journal: International journal of computer assisted radiology and surgery
Published Date: Mar 17, 2021

Abstract

PURPOSE: In quantitative computed tomography (CT), manual selection of the intensity calibration phantom's region of interest is necessary for calculating density (mg/cm) from the radiodensity values (Hounsfield units: HU). However, as this manual process requires effort and time, the purposes of this study were to develop a system that applies a convolutional neural network (CNN) to automatically segment intensity calibration phantom regions in CT images and to test the system in a large cohort to evaluate its robustness.

Authors

  • Keisuke Uemura
    Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan. keisuke-uemura@is.naist.jp.
  • Yoshito Otake
  • Masaki Takao
  • Mazen Soufi
    Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, Nara, Japan.
  • Akihiro Kawasaki
    Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
  • Nobuhiko Sugano
  • Yoshinobu Sato
    Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan. Electronic address: yoshi@is.naist.jp.

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