The segmentation of Region Of Interests (ROIs) in high-resolution histopathological images is a task with high clinical relevance which requires large amounts of reading time from pathologists and suffers from many considerable problems, such as inter and intra-reader variability. Thus, its automation is desirable because it would hugely reduce the workload of the clinicians, while at the same time reduce the subjectivity in diagnosis. However there are still open issues like the amount of data required for training and testing algorithms, the computational load when dealing with high resolution images and, most of all, the large variability in the analyzed data (mainly due to different cell types, cell densities, stains, magnification levels, and so on).The segmentation of ROIs can be expressed as a Semantic Segmentation problem for which Deep Learning models are proven to be particular suitable.However building these models require an huge amount of annotated training data.