A Novel Graphical Model Approach to Segmenting Cell Images

Successful biological image analysis usually requires satisfactory segmentations to identify regions of interest as an intermediate step. Here we present a novel graphical model approach for segmentation of multi-cell yeast images acquired by fluorescence microscopy. Yeast cells are often clustered together, so they are hard to segment by conventional techniques. Our approach assumes that two parallel images are available for each field: an image containing information about the nuclear positions (such as an image of a DNA probe) and an image containing information about the cell boundaries (such as a differential interference contrast, or DIC, image). The nuclear information provides an initial assignment of whether each pixel belongs to the background or one of the cells. The boundary information is used to estimate the probability that any two pixels in the graph are separated by a cell boundary. From these two kinds of information, we construct a graph that links nearby pairs of pixels, and seek to infer a good segmentation from this graph. We pose this problem as inference in a Bayes network, and use a fast approximation approach to iteratively improve the estimated probability of each class for each pixel. The resulting algorithm can efficiently generate segmentation masks which are highly consistent with hand-labeled data, and results suggest that the work will be of particular use for large scale determination of protein location patterns by automated microscopy