Image segmentation of mesenchymal stem cells in diverse culturing conditions

Muhammad Jamal Afridi, Chun Liu, Christina Chan, Seungik Baek, Xiaoming Liu

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

    • 3 Citations

    Abstract

    Researchers in the areas of regenerative medicine and tissue engineering have great interests in understanding the relationship of different sets of culturing conditions and applied mechanical stimuli to the behavior of mesenchymal stem cells (MSCs). However, it is challenging to design a tool to perform automatic cell image analysis due to the diverse morphologies of MSCs. Therefore, as a primary step towards developing the tool, we propose a novel approach for accurate cell image segmentation. We collected three MSC datasets cultured on different surfaces and exposed to diverse mechanical stimuli. By analyzing existing approaches on our data, we choose to substantially extend binarization-based extraction of alignment score (BEAS) approach by extracting novel discriminating features and developing an adaptive threshold estimation model. Experimental results on our data shows our approach is superior to seven conventional techniques. We also define three quantitative measures to analyze the characteristics of images in our datasets. To the best of our knowledge, this is the first study that applied automatic segmentation to live MSC cultured on different surfaces with applied stimuli.

    Original languageEnglish (US)
    Title of host publication2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
    PublisherIEEE Computer Society
    Pages516-523
    Number of pages8
    ISBN (Print)9781479949854
    DOIs
    StatePublished - 2014
    Event2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States

    Other

    Other2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
    CountryUnited States
    CitySteamboat Springs, CO
    Period3/24/143/26/14

    Profile

    Stem cells
    Image segmentation
    Tissue engineering
    Image analysis

    ASJC Scopus subject areas

    • Computer Science Applications
    • Computer Vision and Pattern Recognition

    Cite this

    Afridi, M. J., Liu, C., Chan, C., Baek, S., & Liu, X. (2014). Image segmentation of mesenchymal stem cells in diverse culturing conditions. In 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 (pp. 516-523). [6836058] IEEE Computer Society. DOI: 10.1109/WACV.2014.6836058

    Image segmentation of mesenchymal stem cells in diverse culturing conditions. / Afridi, Muhammad Jamal; Liu, Chun; Chan, Christina; Baek, Seungik; Liu, Xiaoming.

    2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014. IEEE Computer Society, 2014. p. 516-523 6836058.

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

    Afridi, MJ, Liu, C, Chan, C, Baek, S & Liu, X 2014, Image segmentation of mesenchymal stem cells in diverse culturing conditions. in 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014., 6836058, IEEE Computer Society, pp. 516-523, 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, Steamboat Springs, CO, United States, 24-26 March. DOI: 10.1109/WACV.2014.6836058
    Afridi MJ, Liu C, Chan C, Baek S, Liu X. Image segmentation of mesenchymal stem cells in diverse culturing conditions. In 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014. IEEE Computer Society. 2014. p. 516-523. 6836058. Available from, DOI: 10.1109/WACV.2014.6836058

    Afridi, Muhammad Jamal; Liu, Chun; Chan, Christina; Baek, Seungik; Liu, Xiaoming / Image segmentation of mesenchymal stem cells in diverse culturing conditions.

    2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014. IEEE Computer Society, 2014. p. 516-523 6836058.

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

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