Nuclear detection in embryos is an indispensable process for quantitative analysis of the development of multicellular organisms. Due to the overlap in the distribution of pixel intensity of nuclear and cytoplasmic regions and the large variation of pixel intensity even within the same type of cellular components in different embryos, it is difficult to separate nuclear regions from the surrounding cytoplasmic region in differential interference contrast (DIC) microscope image. This study explores a discriminative representation of a local patch around a fixed pixel, called top-ranked intensity-ordered descriptor (TRIOD), which is prospected to distinguish the smoothed texture in the nucleus from the irregular texture in cytoplasm containing yolk granules. Then, a probability process is employed to model nuclear TRIOD prototypes, and the enhanced nuclear probability map can be constructed with the TRIODs of all pixels in a DIC microscope image. Finally, a distance-regularized level set method, which not only considers the probability change in a nearby pixel but also regularizes the contour smoothness, is applied to refine the initial localization by simply thresholding on the enhanced probability map. Experimental results show that the proposed strategy can give much better performance for segmentation of nuclear regions than the conventional strategies.

中文翻译：

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使用排名靠前的强度排序描述符的增强概率图在发育中的胚胎的4D显微镜图像中进行核检测

胚胎中的核检测是定量分析多细胞生物发展的必不可少的过程。由于核和细胞质区域的像素强度分布重叠，并且即使在不同胚胎中的同一类型的细胞成分内，像素强度的变化也很大，因此很难在差分干扰对比中将核区域与周围的细胞质区域分开（DIC）显微镜图像。这项研究探索了在固定像素周围的局部斑块的判别性表示，称为最高等级的强度排序描述符（TRIOD），有望区分包含卵黄颗粒的细胞质中的平滑纹理和不规则纹理。然后，采用概率过程对核TRIOD原型进行建模，并且可以使用DIC显微镜图像中所有像素的TRIOD构建增强的核概率图。最后，不仅考虑附近像素的概率变化，而且使轮廓平滑度正规化的距离正则化水平集方法通过简单地在增强的概率图上进行阈值化来应用于优化初始定位。实验结果表明，与常规策略相比，所提策略在分割核区域方面具有更好的性能。通过简单地对增强的概率图进行阈值化，可以使用来优化初始定位。实验结果表明，与常规策略相比，所提策略在分割核区域方面具有更好的性能。通过简单地对增强的概率图进行阈值化，可以使用来优化初始定位。实验结果表明，与常规策略相比，所提策略在分割核区域方面具有更好的性能。