A two-level approach towards semantic colon segmentation: Removing extra-colonic findings

Le Lu, Matthias Wolf, Jianming Liang, Murat Dundar, Jinbo Bi, Marcos Salganicoff

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    16 Citations (Scopus)

    Abstract

    Computer aided detection (CAD) of colonic polyps in computed tomographic colonography has tremendously impacted colorectal cancer diagnosis using 3D medical imaging. It is a prerequisite for all CAD systems to extract the air-distended colon segments from 3D abdomen computed tomography scans. In this paper, we present a two-level statistical approach of first separating colon segments from small intestine, stomach and other extra-colonic parts by classification on a new geometric feature set; then evaluating the overall performance confidence using distance and geometry statistics over patients. The proposed method is fully automatic and validated using both the classification results in the first level and its numerical impacts on false positive reduction of extra-colonic findings in a CAD system. It shows superior performance than the state-of-art knowledge or anatomy based colon segmentation algorithms.

    Original languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages1009-1016
    Number of pages8
    Volume5762 LNCS
    EditionPART 2
    DOIs
    StatePublished - 2009
    Event12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009 - London, United Kingdom
    Duration: Sep 20 2009Sep 24 2009

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 2
    Volume5762 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
    CountryUnited Kingdom
    CityLondon
    Period9/20/099/24/09

    Fingerprint

    Computer-aided Detection
    Segmentation
    Semantics
    Colorectal Cancer
    3D Imaging
    Medical Imaging
    Medical imaging
    Computed Tomography
    Anatomy
    False Positive
    Tomography
    Confidence
    Statistics
    Geometry
    Air

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Lu, L., Wolf, M., Liang, J., Dundar, M., Bi, J., & Salganicoff, M. (2009). A two-level approach towards semantic colon segmentation: Removing extra-colonic findings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 5762 LNCS, pp. 1009-1016). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5762 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-04271-3_122

    A two-level approach towards semantic colon segmentation : Removing extra-colonic findings. / Lu, Le; Wolf, Matthias; Liang, Jianming; Dundar, Murat; Bi, Jinbo; Salganicoff, Marcos.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5762 LNCS PART 2. ed. 2009. p. 1009-1016 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5762 LNCS, No. PART 2).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Lu, L, Wolf, M, Liang, J, Dundar, M, Bi, J & Salganicoff, M 2009, A two-level approach towards semantic colon segmentation: Removing extra-colonic findings. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 5762 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 5762 LNCS, pp. 1009-1016, 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009, London, United Kingdom, 9/20/09. https://doi.org/10.1007/978-3-642-04271-3_122
    Lu L, Wolf M, Liang J, Dundar M, Bi J, Salganicoff M. A two-level approach towards semantic colon segmentation: Removing extra-colonic findings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 5762 LNCS. 2009. p. 1009-1016. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-04271-3_122
    Lu, Le ; Wolf, Matthias ; Liang, Jianming ; Dundar, Murat ; Bi, Jinbo ; Salganicoff, Marcos. / A two-level approach towards semantic colon segmentation : Removing extra-colonic findings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5762 LNCS PART 2. ed. 2009. pp. 1009-1016 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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    abstract = "Computer aided detection (CAD) of colonic polyps in computed tomographic colonography has tremendously impacted colorectal cancer diagnosis using 3D medical imaging. It is a prerequisite for all CAD systems to extract the air-distended colon segments from 3D abdomen computed tomography scans. In this paper, we present a two-level statistical approach of first separating colon segments from small intestine, stomach and other extra-colonic parts by classification on a new geometric feature set; then evaluating the overall performance confidence using distance and geometry statistics over patients. The proposed method is fully automatic and validated using both the classification results in the first level and its numerical impacts on false positive reduction of extra-colonic findings in a CAD system. It shows superior performance than the state-of-art knowledge or anatomy based colon segmentation algorithms.",
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