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Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
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An Augmented Cell Segmentation in Fluorescent in Situ Hybridization Images

Jianhuo Shen, Teng Li, Chuanrui Hu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed

    Abstract:

    Fluorescence in situ hybridization (FISH) surpass previously available technology to become a foremost biological assay, which can provide reliable imaging biomarkers to diagnose cancer and genetic disorders in the cellular level. In order to guarantee the validity of the quality analysis in cell images, it is significant to accurately segment the cell touching regions. We previously structured a mini-U-net to precisely capture cell regions, but this method sometimes can not separate multiple cells that are attached to each other. This work aims to solve this matter by applying cell identification results to provide more accurate prior information for the watershed to describe the cell boundaries. Validation results on 458 cells showed that Dice coefficients and intersection over union were improved from 81.92% to 83.98% and from 68.34% to 73.83% (p=0.03), respectively. The improved results indicated that cell identification is an effective means to handie the cell touching and produce more accurate cell segmentation.

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