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Títol: Automatic segmentation of Nucleus Accumbens
Autor: Sem, Federico
Tutor/director/avaluador: Igual Muñoz, Laura
Universitat: Universitat Politècnica de Catalunya
Matèries: Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Ciències de la salut ::Medicina::Neurologia
Machine learning
Imaging systems in medicine
Computer vision
Aprenentatge automàtic
Imatges mèdiques
Visió per ordinador
Data: 5-set-2012
Tipus de document: Master thesis
Resum: Segmentation of subcortical structures in the brain has become an increasingly important topic in contemporary medicine. The ability to effi ciently isolate different regions of the human brain has allowed doctors and technicians to become more e fficient in the diagnosis of mental disorders and the evaluation of the patient conditions. An area of the brain whose possible segmentation has received particular attention is the Nucleus Accumbens, which is believed to play a central role in the reward circuit. In fact, studies of volumetric brain magnetic resonance imaging (MRI) have shown neuroanatomical abnormalities of this structure in adult attention defficit/hyperactivity disorder (ADHD), and speci cally a smaller average volume of the region. The use of a reliable automated segmentation method would therefore represent an extremely helpful and e fficient tool for identifying this disorder, especially when compared to manual volume labeling methods, which often turn out to be tedious and extremely time-consuming. However, automatic segmentation of the Accumbens is extremely di fficult to obtain, due to the lack of contrast with the surrounding structures. This means that most conventional segmentation methods are useless for this purpose, and makes the segmentation method selection a very delicate procedure. Consequently, the main objective of the thesis is the implementation of a robust algorithm for segmenting the Nucleus Accumbens structure. The research project aims to apply pre-existing segmentation methods to the Nucleus Accumbens, moving then to an evaluation of such methods and an estimation of how e ffective they are. Diff erent segmentation methods were used for this purpose; firstly, the standard Atlas Segmentation Approach was used, showing generally poor results paired with long computational times and high complexity. Moreover, this method has shown potential problems in the individuation of the correct region, leading, in some cases, to completely wrong segmentations. In addition to the fi rst method, Multi Atlas Segmentation and Adaptive Multi Atlas Segmentation methods have been implemented. The results have shown improved accuracy and better performance than the original method. Judging by the results, the segmentation of the Nucleus Accumbens has proven to be an extremely complicated task, both for the dimension of the structure itself and for the lack of contrast with the surrounding structures. In order to improve detection accuracy, combination of multiple methods is necessary, as using a single method for the segmentation process can lead to an incorrect labeling.
URI: http://hdl.handle.net/2099.1/16445
Condicions d'accés: Open Access
Apareix a les col·leccions:Master in Artificial Intelligence - MAI (Pla 2006)

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