Segmentation of brain MRI structures with deep machine learning

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hdl:2099.1/15836
Tutor / directorIgual Muñoz, Laura
Document typeMaster thesis
Date2012-06-22
Rights accessOpen Access
Except where otherwise noted, content on this work
is licensed under a Creative Commons license
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Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
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).
SubjectsMachine learning, Neural networks (Computer science), Brain--Magnetic resonance imaging, Aprenentatge automàtic, Xarxes neuronals (Informàtica), Cervell--Imatges per ressonància magnètica
DegreeMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2009)
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