基于深度神经网络的人类囊胚图像内细胞团和滋养外胚层分割
Inner Cell Mass and Trophectoderm Segmentation in Human Blastocyst Images using Deep Neural Network
摘要：
基于形态学属性的胚胎质量评估对于体外受精（IVF）获得更高的妊娠率非常重要。胚胎的内细胞团（ICM）和滋养外胚层上皮（TE）的准确分割是重要的，因为这些参数可以帮助预测胚胎的存活力和活产潜力。然而，ICM和TE的分割是困难的，由于它们的形状和纹理的相似性，彼此之间以及与周围环境的变化。为了解决这个问题，实现了一种基于深度神经网络（DNN）的分割方法。DNN可以以99.1%的准确率，94.9%的精确率，93.8%的召回率，94.3%的Dice系数和89.3%的Jaccard指数来识别ICM区域。该算法能够以98.3%的准确率、91.8%的精确率、93.2%的召回率、92.5%的Dice系数和85.3%的Jaccard指数提取TE区域。
英文摘要：
Embryo quality assessment based on morphological attributes is important for achieving higher pregnancy rates from in vitro fertilization (IVF). The accurate segmentation of the embryo's inner cell mass (ICM) and trophectoderm epithelium (TE) is important, as these parameters can help to predict the embryo viability and live birth potential. However, segmentation of the ICM and TE is difficult due to variations in their shape and similarities in their textures, both with each other and with their surroundings. To tackle this problem, a deep neural network (DNN) based segmentation approach was implemented. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
