Semantic segmentation of microscopic images of H&E stained prostatic tissue using CNN

There is a need for an automatic Gleason scoring system that can be used for prostate cancer diagnosis. Today the diagnoses are determined by pathologists manually, which is both a complex and a time-consuming task. To reduce the pathologists' workload, but also to reduce variations between different pathologists, an automatic classification system would be of great use. Some previous works have aimed for this, but still more work needs to be done. It is probable that such a tool would benefit from having access to individually segmented, pathologically relevant objects from the images. Therefore, we have developed an algorithm for semantic segmentation of the microscopic images of H&E stained prostate tissue into Background, Stroma, Epithelial Cytoplasm and Nuclei. This algorithm is based on deep learning, or more specifically a convolutional neural network. The network design is inspired by architectures that previously have been proved successful in different applications. It consists of a contracting and an expanding part, which are symmetrical. We have reached an accuracy of 80 %, as measured by the mean of the intersection over union, for segmentation into four classes. Previous works have only investigated nuclei segmentation, and our network performed similar but for the more challenging task of four class segmentation