Automated estimation of prostate-gland central position using a pelvic cavity shape in X-ray CT images

DOI IR HANDLE
  • NARAMURA Itoshi
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University
  • HAYASHI Tatsuro
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University
  • ZHOU Xiangrong
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University
  • CHEN Huayue
    Department of Anatomy, Division of Disease Control, Graduate School of Medicine, Gifu University
  • HARA Takeshi
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University
  • YOKOYAMA Ryujiro
    Department of Radiology Services, Gifu University Hospital
  • KANEMATSU Masayuki
    Department of Radiology Services, Gifu University Hospital Department of Radiology, Gifu University Hospital
  • HOSHI Hiroaki
    Department of Radiology, Division of Tumor Control, Graduate School of Medicine, Gifu University
  • FUJITA Hiroshi
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University

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Other Title
  • X線CT画像における骨盤腔形状を用いた前立腺の中心位置の自動推定

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Abstract

The automated segmentation of the prostate region in CT images is required by the computer-aided diagnosis and therapeutic radiology. The purpose of this study is to develop an automated scheme for estimating the location (center point) of the prostate gland in torso X-ray CT images. The proposed scheme consists of four processing steps. At first, a prostate gland centroid is extracted manually, and pelvis landmarks are extracted automatically on training dataset. Second, based on the pelvis landmarks, each pelvis shape is normalized. Third, a prostate gland centroid model is constructed by the principal component analysis. Finally, a prostate gland model is applied to a target case, and the center point of prostate gland is estimated. The proposed scheme was applied to 20 CT cases. We found the proposed scheme could estimate the location of the prostate gland with the mean error (3-D distance from the grand truth) of 2.6mm. This scheme was used for prostate gland segmentation using a simple sphere model. From experimental results, the effect of the proposed scheme for estimating the location of the prostate gland was confirmed.

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