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Inner Cell Mass and Trophectoderm Segmentation in Human Blastocyst Images using Deep Neural Network

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Document pages: 6 pages

Abstract: Embryo quality assessment based on morphological attributes is important forachieving higher pregnancy rates from in vitro fertilization (IVF). Theaccurate segmentation of the embryo s inner cell mass (ICM) and trophectodermepithelium (TE) is important, as these parameters can help to predict theembryo viability and live birth potential. However, segmentation of the ICM andTE is difficult due to variations in their shape and similarities in theirtextures, both with each other and with their surroundings. To tackle thisproblem, a deep neural network (DNN) based segmentation approach wasimplemented. The DNN can identify the ICM region with 99.1 accuracy, 94.9 precision, 93.8 recall, a 94.3 Dice Coefficient, and a 89.3 Jaccard Index.It can extract the TE region with 98.3 accuracy, 91.8 precision, 93.2 recall, a 92.5 Dice Coefficient, and a 85.3 Jaccard Index.

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