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Murphy Lab - Automated Classification of 3-Dimensional Protein Location Patterns from Fluorescence Microscope Images

Meel Velliste, graduate student in Biomedical Engineering
Aaron C. Rising, undergraduate student in Biological Sciences


The goal of this work is to develop methods that allow the numerical description and subsequent classification of the patterns characteristic of subcellular structures in fluorescence microscope images of eukaryotic cells. We have previously described classifiers capable of recognizing 2D patterns of all major subcellular structures with high accuracy. This was done with HeLa cells which are fairly flat in the sense that their morphology can be compared to an "egg on a frying pan". However, there are many cell types that have more of a 3D structure such as columnar epithelial cells. A 2D optical section of such cells would hardly be representative of the whole cell. For example, a slice through the middle of the cell would completely miss proteins that localize to either the apical or basal membrane. Therefore if the methods we are developing for systematic analysis of protein location patterns are to be generally useful for all cell types, they will have to be based on full 3D images rather than mere 2D slices. The goal of this project is to extend our methods to work with 3D images.


We first acquired a set of 3D images of HeLa cells using a confocal laser scanning microscope. Seven different fluorescent markers were used to label some of the major subcellular structures and 50 3D stacks of images were collected for each of the classes.

We then adapted our previously used features for use with 3D images. In our 2D classification work we had used three kinds of features: Texture features, Moments and Morphological features. The morphological features had been found the most useful single subset of features for 2D images. Many of these features describe relationships between objects in the image in terms of sizes of objects and distances between them. Therefore as a starting point we extended a subset of these Morphological features by changing the way distances and sizes were calculated. The size of objects was changed to be the volume instead of area. For each feature that described the pattern in terms of distances between objects, two new features were created: one that considers "horizontal" distances (euclidean distance by x,y-coordinates); and another that describes the "vertical" distance or the z-component. These features were calculated for all of the images in the 3D set and then a backpropagation neural network classifier was trained to recognize the seven different classes of patterns.


The neural network classifier was found to be capable of recognizing the 3D subcellular location patterns with an average accuracy of 92%. In order to see if this 3D classification approach has any advantage over 2D classification, we created a 2D comparable image set by taking one optical section from each of the 3D stacks. The section was chosen to intersect the center of fluorescence of each image, because we found that this provided the best classification accuracy. These 2D comparable image were recognized correctly only 87% of the time.


These results demonstrate the feasibility of recognizing protein location patterns in 3D. Furthermore it is clear that there is a great advantage in using 3D images instead of 2D images. If the 3D vs. 2D classification accuracy is 5% better for flat cells like the HeLa cells used here, then the difference will be even more significant for most other cell types where a single 2D slice would be extremely under-representative of the cell. Therefore the 3D features developed here will an invaluable tool when generalizing the automated image interpretation methods for use with different cell types.

Last Updated: 01 Dec 2004

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