Automatic handling of tissue microarray cores in high-dimensional microscopy images.

This paper describes a specific tool for automatically segmenting and archiving of tissue microarray (TMA) cores in microscopy images at different magnifications. TMA enables researchers to extract the small cylinders of a single tissue (core sections) from histological sections and arrange them in an array on a paraffin block such that hundreds can be analyzed simultaneously. A crucial step to improve the speed and quality of this process is the correct localization of each tissue core in the array. However, usually the tissue cores are not aligned in the microarray, the TMA cores are incomplete and the images are noisy and with distorted colors. We develop a robust framework to handle core sections under these conditions. The algorithms are able to detect, stitch, and archive the TMA cores at different magnifications. Once the TMA cores are segmented they are stored in a relational database allowing their processing for further studies of benign-malignant classification. The method was shown to be reliable for handling the TMA cores and therefore enabling further large-scale molecular pathology research.