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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
Imaging systems in medicine
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|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
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
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.|
|Condicions d'accés: ||Open Access|
|Apareix a les col·leccions:||Master in Artificial Intelligence|