Segmentation of brain MRI structures with deep machine learning

Several studies on brain Magnetic Resonance Images (MRI) show relations between neuroanatomical abnormalities of brain structures and neurological disorders, such as Attention De fficit Hyperactivity Disorder (ADHD) and Alzheimer. These abnormalities seem to be correlated with the size and shape of these structures, and there is an active fi eld of research trying to find accurate methods for automatic MRI segmentation. In this project, we study the automatic segmentation of structures from the Basal Ganglia and we propose a new methodology based on Stacked Sparse Autoencoders (SSAE). SSAE is a strategy that belongs to the family of Deep Machine Learning and consists on a supervised learning method based on an unsupervisely pretrained Feed-forward Neural Network. Moreover, we present two approaches based on 2D and 3D features of the images. We compare the results obtained on the di fferent regions of interest with those achieved by other machine learning techniques such as Neural Networks and Support Vector Machines. We observed that in most cases SSAE improves those other methods. We demonstrate that the 3D features do not report better results than the 2D ones as could be thought. Furthermore, we show that SSAE provides state-of-the-art Dice Coe fficient results (left, right): Caudate (90.6+-3 1.4, 90.31 +-1.7), Putamen (91.03 +-1.4, 90.82+- 1.4), Pallidus (85.11+-1.8, 83.47 +-2.2), Accumbens (74.26+- 4.4, 74.46 +-4.6)

Markov Dependence Tree-Based Segmentation of Deep Brain Structures

Abstract. We propose a new framework for multi-object segmentation of deep brain structures, which have significant shape variations and relatively small sizes in medical brain images. In the images, the structure boundaries may be blurry or even missing, and the surrounding background is a clutter and full of irrelevant edges. We suggest a templatebased framework, which fuses the information of edge features, region statistics and inter-structure constraints to detect and locate all the targeted brain structures such that manual initialization is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree. It makes the matching of multiple objects efficient. Our approach needs only one example as training data and alleviates the demand of a large training set. The obtained segmentation results on real data are encouraging and the proposed method enjoys several important advantages over existing methods.