Sub-cortical brain structure segmentation using F-CNN's
This addresses segmentation accuracy for medical imaging applications, but is incremental as it builds on existing F-CNN architectures.
The paper tackled sub-cortical brain structure segmentation in MRI by adapting a Fully-Convolutional Neural Network to operate on full 2D images without alignment, and improved results by integrating it with a Markov Random Field for volumetric homogeneity, showing promising results on two datasets.
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.