Zhang Chong, Yarkony Julian, Hamprecht Fred A
Med Image Comput Comput Assist Interv. 2014;17(Pt 1):9-16. doi: 10.1007/978-3-319-10404-1_2.
Cell detection and segmentation in microscopy images is important for quantitative high-throughput experiments. We present a learning-based method that is applicable to different modalities and cell types, in particular to cells that appear almost transparent in the images. We first train a classifier to detect (partial) cell boundaries. The resulting predictions are used to obtain superpixels and a weighted region adjacency graph. Here, edge weights can be either positive (attractive) or negative (repulsive). The graph partitioning problem is then solved using correlation clustering segmentation. One variant we newly propose here uses a length constraint that achieves state-of-art performance and improvements in some datasets. This constraint is approximated using non-planar correlation clustering. We demonstrate very good performance in various bright field and phase contrast microscopy experiments.
显微镜图像中的细胞检测和分割对于定量高通量实验很重要。我们提出了一种基于学习的方法，该方法适用于不同的模态和细胞类型，特别是在图像中几乎呈现透明的细胞。我们首先训练一个分类器来检测（部分）细胞边界。所得预测结果用于获取超像素和加权区域邻接图。在这里，边权重可以是正的（吸引性的）或负的（排斥性的）。然后使用相关聚类分割来解决图划分问题。我们在此新提出的一个变体使用了长度约束，该约束在某些数据集上实现了领先的性能和改进。此约束通过非平面相关聚类进行近似。我们在各种明场和相差显微镜实验中展示了非常好的性能。
