Segmentation of mitochondria in Serial Section Electron Microscopy images of the brain using Deep Neural Networks

This master thesis approaches the problem of image segmentation by using Deep Neural Networks. Recently applied to a wide variety of problems, these networks have surpassed the previous state-of-the-art performance in fields like computer vision, natural language processing or audio analysis. In particular, our work focuses on the segmentation of mitochondria in Serial Section Electron Microscopy images of the brain. Segmentation is a highly relevant task in medical image analysis, as automatic delineation of organs and structures of interest is often necessary to perform computer assisted diagnosis. All the experiments performed make use of the public Electron Microscopy Dataset, a representation of a section taken from the CA1 hippocampus region of the brain. To this end, it is proposed a new encoder-decoder architecture on which the performance of various loss functions is studied. This network is basically a simplified version of others architectures presented previously in literature, achieving results close to the state-of-the-art over the same dataset used for this study. The results obtained provide evidence that those loss functions which take into account the class imbalance problem perform much better than those which do not take into account the class distribution. This document also depicts the current state-of-the-art of deep learning architectures and optimization techniques for image segmentation