歯科パノラマX線写真における頸動脈石灰化の自動検出:-手動ROIを用いた検出性能の検証-  [in Japanese] Automated detection for carotid artery calcifications in dental panoramic radiographs:Verification of detection performance using manual ROIs  [in Japanese]

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Author(s)

    • 服部 佑香 HATTORI Yuka
    • 岐阜大学工学部応用情報学科 Department of Information Science, Faculty of Engineering, Gifu University
    • 村松 千左子 MURAMATSU Chisako
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 高橋 龍 TAKAHASHI Ryo
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 原 武史 HARA Takeshi
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 周 向栄 ZHOU Xiangrong
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 藤田 広志 FUJITA Hiroshi
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University

Abstract

It is desired to develop a computer‐aided detection system that automatically detects carotid artery calcifications(CACs)on dental panoramic radiographs(DPRs). In our previous study, an automated method for the detection of CACs was proposed, in which the sensitivity of CAC detection was 90 % with 4.6 false positives(FPs)per image. We noticed that the regions of interest(ROIs)determined automatically as possible carotid artery regions by our previous scheme were relatively larger than those estimated by a dental radiologist. In this paper, we verified the necessity of an introduction of appropriate size and location of ROIs which are similar with those by the dental radiologist. We found that the sensitivity was maintained along with those appropriate ROIs, but the number of FPs per image was reduced to 0.9 using 100 DPRs. This result indicates that it is possible to reduce the number of FPs by accurately tailoring ROIs to individual cases.

Journal

  • Medical Imaging and Information Sciences

    Medical Imaging and Information Sciences 32(3), 68-70, 2015

    MEDICAL IMAGING AND INFORMATION SCIENCES

Codes

  • NII Article ID (NAID)
    130005099503
  • Text Lang
    JPN
  • ISSN
    0910-1543
  • Data Source
    J-STAGE 
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