Automated identification of microstructures on histology slides

Grading of breast cancer and the subsequent treatment options are largely dependent on the pathological examination of the histology slides from the tumor tissue. Tumor grading is currently based on the spatial organization of the tissue, including the distribution of cancer cells, the morphological properties of their nuclei and the presence/absence of cancer-associated surface receptors these cells express. In this study, we have developed an automated image processing method to detect and identify clinically relevant microscopic structures on histology slides. The tissue components identified with our program are as follows: fat cells, stroma, and three morphologically distinct cell nuclei types used in grading cancer on the haematoxylin and eosin (H&E) stained slides. The image processing is based on gray-scale segmentation, feature extraction, supervised learning, subsequent training and clustering. Our automated processing system has an accuracy of 89% ± 0.8 in correctly identifying the three different nuclei types observed in H & E stained histology slides.