Deep convolutional neural networks for human embryonic cell counting

Aisha Khan*, Stephen Gould, Mathieu Salzmann

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    52 Citations (Scopus)

    Abstract

    We address the problem of counting cells in time-lapse microscopy images of developing human embryos. Cell counting is considered as an important step in analyzing biological phenomenon such as embryo viability. Traditional approaches to counting cells rely on hand crafted features and cannot fully take advantage of the growth in data set sizes. In this paper, we propose a framework to automatically count the number of cells in developing human embryos. The framework employs a deep convolutional neural network model trained to count cells from raw microscopy images. We demonstrate the effectiveness of our approach on a data set of 265 human embryos. The results show that the proposed framework provides robust estimates of the number of cells in a developing embryo up to the 5-cell stage (i.e., 48 h post fertilization).

    Original languageEnglish
    Title of host publicationComputer Vision - ECCV 2016 Workshops, Proceedings
    EditorsGang Hua, Hervé Jégou
    PublisherSpringer Verlag
    Pages339-348
    Number of pages10
    ISBN (Print)9783319466033
    DOIs
    Publication statusPublished - 2016
    EventComputer Vision - ECCV 2016 Workshops, Proceedings - Amsterdam, Netherlands
    Duration: 8 Oct 201616 Oct 2016

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9913 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    ConferenceComputer Vision - ECCV 2016 Workshops, Proceedings
    Country/TerritoryNetherlands
    CityAmsterdam
    Period8/10/1616/10/16

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