Automatic in vivo cell detection in MRI

Muhammad Jamal Afridi, Xiaoming Liu, Erik Shapiro, Arun Ross

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

    • 2 Citations

    Abstract

    Due to recent advances in cell-based therapies, non-invasive monitoring of in vivo cells in MRI is gaining enormous interest. However, to date, the monitoring and analysis process is conducted manually and is extremely tedious, especially in the clinical arena. Therefore, this paper proposes a novel computer vision-based learning approach that creates superpixel-based 3D models for candidate spots in MRI, extracts a novel set of superfern features, and utilizes a partition-based Bayesian classifier ensemble to distinguish spots from non-spots. Unlike traditional ferns that utilize pixel-based differences, superferns exploit superpixel averages in computing difference-based features despite the absence of any order in superpixel arrangement. To evaluate the proposed approach, we develop the first labeled database with a total of more than 16 thousand labels on five in vivo and four in vitro MRI scans. Experimental results show the superiority of our approach in comparison to the two most relevant baselines. To the best of our knowledge, this is the first study to utilize a learning-based methodology for in vivo cell detection in MRI.

    Original languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    PublisherSpringer Verlag
    Pages391-399
    Number of pages9
    Volume9351
    ISBN (Print)9783319245737
    DOIs
    StatePublished - 2015
    Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9351
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
    CountryGermany
    CityMunich
    Period10/5/1510/9/15

    Profile

    Cell
    Magnetic resonance imaging
    Monitoring
    Classifier ensemble
    Bayesian classifier
    3D model
    Computer vision
    Therapy
    Baseline
    Arrangement
    Pixel
    Partition
    Methodology
    Computing
    Evaluate
    Experimental results
    Labels
    Classifiers
    Pixels

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Afridi, M. J., Liu, X., Shapiro, E., & Ross, A. (2015). Automatic in vivo cell detection in MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9351, pp. 391-399). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351). Springer Verlag. DOI: 10.1007/978-3-319-24574-4_47

    Automatic in vivo cell detection in MRI. / Afridi, Muhammad Jamal; Liu, Xiaoming; Shapiro, Erik; Ross, Arun.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9351 Springer Verlag, 2015. p. 391-399 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351).

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

    Afridi, MJ, Liu, X, Shapiro, E & Ross, A 2015, Automatic in vivo cell detection in MRI. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9351, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9351, Springer Verlag, pp. 391-399, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, Munich, Germany, 5-9 October. DOI: 10.1007/978-3-319-24574-4_47
    Afridi MJ, Liu X, Shapiro E, Ross A. Automatic in vivo cell detection in MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9351. Springer Verlag. 2015. p. 391-399. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Available from, DOI: 10.1007/978-3-319-24574-4_47

    Afridi, Muhammad Jamal; Liu, Xiaoming; Shapiro, Erik; Ross, Arun / Automatic in vivo cell detection in MRI.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9351 Springer Verlag, 2015. p. 391-399 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351).

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

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    abstract = "Due to recent advances in cell-based therapies, non-invasive monitoring of in vivo cells in MRI is gaining enormous interest. However, to date, the monitoring and analysis process is conducted manually and is extremely tedious, especially in the clinical arena. Therefore, this paper proposes a novel computer vision-based learning approach that creates superpixel-based 3D models for candidate spots in MRI, extracts a novel set of superfern features, and utilizes a partition-based Bayesian classifier ensemble to distinguish spots from non-spots. Unlike traditional ferns that utilize pixel-based differences, superferns exploit superpixel averages in computing difference-based features despite the absence of any order in superpixel arrangement. To evaluate the proposed approach, we develop the first labeled database with a total of more than 16 thousand labels on five in vivo and four in vitro MRI scans. Experimental results show the superiority of our approach in comparison to the two most relevant baselines. To the best of our knowledge, this is the first study to utilize a learning-based methodology for in vivo cell detection in MRI.",
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