Automatic Handling of Tissue Microarray Cores in High-Dimensional Microscopy Images

Abstract

AbstractThis paper describes a specific tool to automatically perform the segmentation and archiving of tissue microarray (TMA) cores in microscopy images at different magnification, that is, 5x, 10x, 20x and 40x. TMA enables researchers to extract small cylinder of single tissues (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 these analyses is the correct recognition of each tissue position in the array. However, usually the tissue cores are not aligned in the microarray, the TMA cores are broken and the digital images are noisy. We develop a robust framework to handle core sections under these conditions. The algorithms are able to detect, stitch and archive the TMA cores. Once the TMA cores are segmented they are stored in a relational database allowing their location and classification 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 investigations.KeywordsRelational DatabaseTissue MicroarrayTissue CoreCore SectionRigid RegistrationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.