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

BACKGROUND: Knowledge of the subcellular location of a protein is critical to understanding how that protein works in a cell. This location is frequently determined by the interpretation of fluorescence microscope images. In recent years, automated systems have been developed for consistent and objective interpretation of such images so that the protein pattern in a single cell can be assigned to a known location category. While these systems perform with nearly perfect accuracy for single cell images of all major subcellular structures, their ability to distinguish subpatterns of an organelle (such as two Golgi proteins) is not perfect. Our goal in the work described here was to improve the ability of an automated system to decide which of two similar patterns is present in a field of cells by considering more than one cell at a time. Since cells displaying the same location pattern are often clustered together, considering multiple cells may be expected to improve discrimination between similar patterns. RESULTS: We describe how to take advantage of information on experimental conditions to construct a graphical representation for multiple cells in a field. Assuming that a field is composed of a small number of classes, the classification accuracy can be improved by allowing the computed probability of each pattern for each cell to be influenced by the probabilities of its neighboring cells in the model. We describe a novel way to allow this influence to occur, in which we adjust the prior probabilities of each class to reflect the patterns that are present. When this graphical model approach is used on synthetic multi-cell images in which the true class of each cell is known, we observe that the ability to distinguish similar classes is improved without suffering any degradation in ability to distinguish dissimilar classes. The computational complexity of the method is sufficiently low that improved assignments of classes can be obtained for fields of twelve cells in under 0.04 second on a 1600 megahertz processor. CONCLUSION: We demonstrate that graphical models can be used to improve the accuracy of classification of subcellular patterns in multi-cell fluorescence microscope images. We also describe a novel algorithm for inferring classes from a graphical model. The performance and speed suggest that the method will be particularly valuable for analysis of images from high-throughput microscopy. We also anticipate that it will be useful for analyzing the mixtures of cell types typically present in images of tissues. Lastly, we anticipate that the method can be generalized to other problems.

A protein molecule is made of a long chain of amino acid sequences that fold into a complex three-dimensional structure. It is often the geometrical shapes that determine the protein functions. In molecular biology, researchers use sequence alignment and structure matching to compare the similarity among proteins. Considering proteins as 3D structures, we propose a novel algorithm to identify geometry-based features to retrieve similar proteins without having to deal with complex chemical characteristics and biological properties. A Web-based Dali server, which performs well in three-dimensional structure matching, is used as the ground truth to evaluate our algorithm. Our system performs close to the ground truth with much simplicity and efficiency.