We present a novel method for identification of the boundary of embryonic cells (blastomeres) in Hoffman Modulation Contrast (HMC) microscopic images that are taken between day one to day three. Identification of boundaries of blastomeres is a challenging task, especially in the cases containing four or more cells. This is because these cells are bundled up tightly inside an embryo's membrane and any 2D image projection of such 3D embryo includes cell overlaps, occlusions, and projection ambiguities. Moreover, human embryos include fragmentation, which does not conform to any specific patterns or shape. Here we developed a model-based iterative approach, in which blastomeres are modeled as ellipses that conform to the local image features, such as edges and normals. In an iterative process, each image feature contributes only to one candidate and is removed upon being associated to a model candidate. We have tested the proposed algorithm on an image dataset comprising of 468 human embryos obtained from different sources. An overall Precision, Sensitivity and Overall Quality (OQ) of 92%, 88% and 83% are achieved.
翻译:我们在Hoffman Modulate Contrast (HMC) 的显微镜图象中提出了一种新的方法,用以确定胚胎细胞的界限(blastomeres),这些图象是在一天到三天之间拍摄的。 辨别爆炸粒子的界限是一项具有挑战性的任务, 特别是在含有四个或四个以上细胞的情况下。 这是因为这些细胞被紧紧地捆绑在胚胎的膜膜内, 任何这种3D胚胎的2D图像投影都包含细胞重叠、 隔离和投影模糊。 此外, 人类胚胎包括分裂, 不符合任何特定模式或形状。 我们在这里开发了一种基于模型的迭接合方法, 使爆炸粒子模拟为符合当地图像特征的椭圆形, 例如边缘和正常。 在迭接动过程中, 每个图像特征只对一名候选者有用, 并在与模型候选者联系起来时被删除。 我们测试了由不同来源的468个人类胚胎组成的图像数据集的算法。 总体精度、 感知性和总体质量( OQ) 达到92%、 88%和83%。