Accurate segmentation of touching cells in multi-channel microscopy images with geodesic distance based clustering

Multi-channel microscopy images have been widely used for drug and target discovery in biomedical studies by investigating morphological changes of individual cells. However, it is still challenging to segment densely touching individual cells in such images accurately and automatically. Herein, we propose a geodesic distance based clustering approach to efficiently segmenting densely touching cells in multi-channel microscopy images. Specifically, an adaptive learning scheme is introduced to iteratively adjust the clustering centers which can significantly improve the segmentation accuracy of cell boundaries. Moreover, a novel seed selection procedure based on nuclei segmentation is suggested to determine the true number of cells in an image. To validate this proposed method, we applied it to segment the touching Madin-Darby Canine Kidney (MDCK) epithelial cells in multi-channel images for measuring the distinct N-Ras protein expression patterns inside individual cells. The experimental results demonstrated its advantages on accurately segmenting massive touching cells, as well as the robustness to the low signal-to-noise ratio and varying intensity contrasts in multi-channel microscopy images. Moreover, the quantitative comparison showed its superiority over the typical existing cell segmentation methods.