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Cleavage and Blastulation01:33

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After a large-single-celled zygote is produced via fertilization, the process of cleavage occurs while zygotes travel through the uterine tube. Cleavage is a mitotic cell division that does not result in growth. With each round of successive cell division, daughter cells get increasingly smaller.
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Protocol for Human Blastoids Modeling Blastocyst Development and Implantation
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Automatic Identification of Human Blastocyst Components via Texture.

Parvaneh Saeedi, Dianna Yee, Jason Au

    IEEE Transactions on Bio-Medical Engineering
    |October 10, 2017
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    This study presents an automated algorithm to segment human blastocysts, identifying the trophectoderm (TE) and inner cell mass (ICM). This method improves objective embryo assessment in in vitro fertilization (IVF) and aids research into pregnancy success rates.

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    Area of Science:

    • Reproductive biology
    • Medical imaging
    • Computational biology

    Background:

    • Embryo selection in human in vitro fertilization (IVF) is crucial for maximizing pregnancy rates.
    • Current morphological assessment of blastocysts by embryologists is subjective and poses quality control challenges.
    • Accurate identification of key blastocyst components, trophectoderm (TE) and inner cell mass (ICM), is vital for embryo viability assessment.

    Purpose of the Study:

    • To develop an automated algorithm for segmenting the TE and ICM in human blastocysts.
    • To overcome the challenges posed by the similar textures and interconnectedness of TE and ICM regions.
    • To enable a more detailed and quantitative assessment of blastocyst characteristics for improved IVF outcomes.

    Main Methods:

    • An algorithm was developed to automatically segment TE and ICM regions of day-5 human blastocysts.
    • The algorithm utilizes texture information, along with biological and physical embryo characteristics.
    • Segmentation is based on identifying intrinsic properties of TE and ICM regions.

    Main Results:

    • The algorithm achieved an accuracy of 86.6% for identifying TE regions.
    • The algorithm achieved an accuracy of 91.3% for identifying ICM regions.
    • The study was validated on a dataset of 211 blastocyst images.

    Conclusions:

    • Automated segmentation of TE and ICM offers a more objective and detailed method for blastocyst assessment in IVF.
    • This quantitative approach can help correlate blastocyst features with pregnancy outcomes.
    • The findings support future research into predicting embryo success rates and improving IVF efficacy.