Sub cortical brain structure segmentation using F CNN'S
In this paper we propose a deep learning approach for segmenting sub cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state of the art Fully Convolutional Neural Network (F CNN) architecture for semantic segmentation of objects in natural images, and adapt it to our task. Unlike previous CNN based methods that operate on image patches, our model is applied on a full blown 2D image, without any alignment or registration steps at testing time. We further improve segmentation results by interpreting the CNN output as potentials of a Markov Random Field (MRF) whose topology corresponds to a volumetric grid. Alpha expansion is used to perform approximate inference imposing spatial volumetric homogeneity to the CNN priors. We compare the performance of the proposed pipeline with a similar system using Random Forest based priors, as well as state of art segmentation algorithms, and show promising results on two different brain MRI datasets.
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)