利用显微图像自动评价人类卵母细胞发育潜能
Automatic evaluation of human oocyte developmental potential from microscopy images
摘要：
不孕症正在成为越来越多夫妇的问题。最常见的解决方案是体外受精，需要胚胎学家仔细检查人类卵母细胞的光学显微镜图像，以确定其发育潜力。我们提出了一个自动系统，以提高速度，重复性和准确性，这一过程。我们首先使用CNN（U-Net）分割定位单个卵母细胞并识别其主成分。接下来，我们计算几个基于几何和纹理的描述符。最后一步是SVM分类器。分割和分类训练都基于专家注释。所提出的方法导致70%的分类准确率。
英文摘要：
Infertility is becoming an issue for an increasing number of couples. The most common solution, in vitro fertilization, requires embryologists to carefully examine light microscopy images of human oocytes to determine their developmental potential. We propose an automatic system to improve the speed, repeatability, and accuracy of this process. We first localize individual oocytes and identify their principal components using CNN (U-Net) segmentation. Next, we calculate several descriptors based on geometry and texture. The final step is an SVM classifier. Both the segmentation and classification training is based on expert annotations. The presented approach leads to a classification accuracy of 70%.
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