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An accurate and standardized technique for tumor tissue segmentation is a critical step for monitoring and quantifying the activity of specific families of proteins involved in multi-factorial genetic pathologies. However, fully automated tissue and cell segmentation in clinical images presents many challenges related to the characteristics of the images that make traditional approaches substantially ineffective or incomplete. In this paper we present a fully-automated algorithm that is able to perform accurate and fast segmentation of tissue images. Experimental results on several real-life datasets demonstrate the high level of accuracy achievable thanks to our approach.