Psoas major muscle segmentation using higher-order shape prior

Tsutomu Inoue*, Yoshiro Kitamura, Yuanzhong Li, Wataru Ito, Hiroshi Ishikawa

*この研究の対応する著者

    研究成果: Conference contribution

    5 被引用数 (Scopus)

    抄録

    We propose a novel segmentation method based on higher-order graph cuts which enables the utilization of prior knowledge regarding anatomical shapes. We applied the method for segmentation of psoas major muscles by using combinations of logistic curves which representing their shapes. The higher-order terms consisting of variables (voxels) just inside or outside of the estimated shapes are added to the energy function to encourage the segmentation results to fit to the shapes. We verified the effectiveness of the method with 20 abdominal CT images. By comparing the segmentation results to the ground truth data prepared by a clinical expert, we validated the method where it achieved the Jaccard similarity coefficient (JSC) of 75.4 % (right major) and 77.5 % (left major). We also confirmed that the proposed method worked well for thick CT images.

    本文言語English
    ホスト出版物のタイトルMedical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers
    出版社Springer Verlag
    ページ116-124
    ページ数9
    9601
    ISBN(印刷版)9783319420158
    DOI
    出版ステータスPublished - 2016
    イベントInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI - Germany, Germany
    継続期間: 2015 10 92015 10 9

    出版物シリーズ

    名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    9601
    ISSN(印刷版)03029743
    ISSN(電子版)16113349

    Other

    OtherInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
    国/地域Germany
    CityGermany
    Period15/10/915/10/9

    ASJC Scopus subject areas

    • 理論的コンピュータサイエンス
    • コンピュータ サイエンス(全般)

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