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    Point supervised extended scenario nuclear analysis framework based on LSTM-CFCN

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    Author
    Sui, D.
    Guo, M.
    Zhang, L.
    Date
    2020
    Journal
    IEEE Access
    Publisher
    Institute of Electrical and Electronics Engineers Inc.
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1109/ACCESS.2020.2984996
    Abstract
    Cells and cell like particles detection and segmentation are of significant interest to many biological and clinical studies. Traditionally, these tasks are usually performed by visual inspection, which is time consuming, labor intensity and prone to induce subjective bias between different people. These make automatic cell analysis protocols essential for large-scale and objective studies. In recent years, imaging technical has been significantly advanced following the great success by computer vision. In addition, these technologies enable the cross module microscopy analysis, and make the task of cell analysis extremely challenging. Over these decades, computer aided cell detection, counting and segmentation have evolved from earlier filter based methods to the state-of-art deep learning protocols. However, there are still few suitable frameworks that can process multiple source cell images at the same time. In this paper, we seek a different route and propose a novel efficient framework for robust cell analysis based on Long Short Term Memory Channeled Fully Convolution Neural Networks (LSTM-CFCN). The results demonstrates that our framework is able to perform most of cell detection, counting and segmentation tasks from different cell type, and it can also cover most kinds of microscopy images scenarios including dark field, bright field, pathological and electron images. We have perform substantial experiments on several benchmark datasets, the LSTM-CFCN achieves the highest or at least top-2 performance in terms of F1-score, compared with other state-of-the-art methods.
    Sponsors
    This work was supported in part by the National Natural Science Foundation of China under Grant 61702026, in part by the Natural Science Foundation of Shandong Province under Grant ZR2019MF011, and in part by the China Postdoctoral Science Foundation Project under Grant 2017M622210.
    Keyword
    Cell analysis
    LSTM-CFCN
    multi-scale image processing
    multi-task learning
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084924567&doi=10.1109%2fACCESS.2020.2984996&partnerID=40&md5=721fbe7771c4cb729bcbc2e8e9ed4656; http://hdl.handle.net/10713/12916
    ae974a485f413a2113503eed53cd6c53
    10.1109/ACCESS.2020.2984996
    Scopus Count
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