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Sub-cortical brain structure segmentation using F-CNN's

Abstract : 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.
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Contributor : Enzo Ferrante Connect in order to contact the contributor
Submitted on : Friday, February 5, 2016 - 2:10:14 PM
Last modification on : Saturday, June 25, 2022 - 7:41:09 PM
Long-term archiving on: : Friday, November 11, 2016 - 11:04:40 PM


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Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License


  • HAL Id : hal-01265500, version 1
  • ARXIV : 1602.02130


Mahsa Shakeri, Stavros Tsogkas, Enzo Ferrante, Sarah Lippe, Samuel Kadoury, et al.. Sub-cortical brain structure segmentation using F-CNN's. ISBI 2016 - International Symposium on Biomedical Imaging, 2016, Prague, Czech Republic. ⟨hal-01265500⟩



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