论文标题
使用深神经网络中的人胚泡图像中的内部细胞质量和滋养剂分割
Inner Cell Mass and Trophectoderm Segmentation in Human Blastocyst Images using Deep Neural Network
论文作者
论文摘要
基于形态学属性的胚胎质量评估对于通过体外受精（IVF）实现较高的妊娠率很重要。对胚胎内部细胞质量（ICM）和滋养外胚层上皮（TE）的准确分割很重要，因为这些参数可以帮助预测胚胎生存能力和活产势。但是，由于其形状的变化和质地的相似性，彼此之间和周围环境的变化，ICM和TE的分割很困难。为了解决这个问题，实施了基于深神经网络（DNN）的细分方法。 DNN可以以99.1％的精度，94.9％的精度，93.8％的召回，94.3％的骰子系数和89.3％的Jaccard指数识别ICM区域。它可以以98.3％的精度，91.8％的精度，93.2％的召回，92.5％的骰子系数和85.3％的Jaccard指数提取TE区域。
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.
