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Resources » Paper

Yang S et al. (2019) J Comput Biol "Three-Dimensional Embryonic Image Segmentation and Registration Based on Shape Index and Ellipsoid-Fitting Method."

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  • Overview

    PMID:
    30526025
    DOI:
    10.1089/cmb.2018.0165
    Status:
    Publication type:
    Journal_article
    WormBase ID:
    WBPaper00060466

    Yang S, Han X, & Chen Y (2019). Three-Dimensional Embryonic Image Segmentation and Registration Based on Shape Index and Ellipsoid-Fitting Method. J Comput Biol, 26, 128-142. doi:10.1089/cmb.2018.0165

    Quantitative analysis based on three-dimensional differential interference contrast (DIC) images is currently the mainstream in analyzing gene functions involved in early cell fate specifications. Segmentation and registration are the two most important steps in analysis. Many image segmentation methods have poor performance on embryonic DIC images because of the interference of egg shells, blurs, and nonuniform intensity background. A novel segmentation method is presented based on the shape index (SI) of local intensity variation in DIC images. To compute the SI, the intensity values of a local neighborhood are regarded as z coordinates over x-y planes. After calculating the SI for each pixel by evaluating the shape of intensity surface over the corresponding local neighborhood, SI thresholding is used to detect cytoplasm granules within embryonic boundaries. As a scalar and rotation invariant, the SI is both insensitive to directional changes and different ranges of intensity variations. Embryonic registration methods are usually based on the orientation of vertebrate anteroposterior (AP) axes computed from segmented embryonic boundaries. Due to the blur of marginal slices in DIC images, usually the segmented boundaries are incomplete, which may make the computed AP axes shift away from the correct orientation when using the principal component analysis method. A method calculating AP axes based on ellipsoid-fitting is proposed, which can achieve high accuracy when handling incomplete segmented boundaries. Using Worm Developmental Dynamics Database, we evaluated the performance of the proposed segmentation method and the computation of AP axes. Experimental results show that the two methods outperform existing methods.


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