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Author: | Sem, Federico |
Title: | Automatic segmentation of nucleus accumbens |
Publication type: | Master's thesis |
Publication year: | 2012 |
Pages: | [7] + 123 Language: eng |
Department/School: | Tietotekniikan laitos |
Main subject: | Informaatiotekniikka (T-61) |
Supervisor: | Oja, Erkki ; Igual, Laura |
Instructor: | Vigario, Ricardo |
OEVS: | Electronic archive copy is available via Aalto Thesis Database.
Instructions Reading digital theses in the closed network of the Aalto University Harald Herlin Learning CentreIn the closed network of Learning Centre you can read digital and digitized theses not available in the open network. The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/ You can read theses on the Learning Centre customer computers, which are available on all floors.
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Location: | P1 Ark Aalto 7078 | Archive |
Keywords: | nucleus accumbens segmentation manual automatic Atlas MRI |
Abstract (eng): | Segmentation of subcortical structures in the brain through MRI scans has become an increasingly important topic in contemporary medicine. An area of the brain whose possible segmentation has received particular attention is the Nucleus Accumbens (NA), which is believed to play a central role in the reward circuit. The use of a reliable automated segmentation method would represent an extremely helpful and efficient tool for medical teams, since many disorders (such as ADHD) cause variations in the volume of the NA. Consequently, the main objective of the thesis is the implementation of a robust algorithm for segmenting the Nucleus Accumbens structure. We apply state of the art segmentation methods to the NA, using three different methods; firstly, the standard Atlas Segmentation Approach is used, showing generally poor results paired with long computational times and high complexity. In addition, Multi Atlas Segmentation and Adaptive Multi Atlas Segmentation methods are implemented. The results show improved accuracy and better performance than the single Atlas approach. Finally, we propose a prototype of GUI aimed at implementing a semi supervised segmentation approach, aimed at exploiting the knowledge of human experts to refine the segmentation results, and targeted to medical use. |
ED: | 2012-11-06 |
INSSI record number: 45388
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