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Machine Learning vs Deep Learning for Fully Automatic Image Segmentation in Neurosurgery

Mieye Tyrone Ombe

Machine Learning vs Deep Learning for Fully Automatic Image Segmentation in Neurosurgery.

Rel. Anna Filomena Carbone, Diego Garbossa. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2020

Abstract:

Segmentation of brain tumor is a delicate procedure which requires a very high degree of accuracy. This thesis aims at studying the intricacies of automatic segmentation of brain Magnetic Resonance Images with the ultimate goal of building a fully automatic segmentation software whose accuracy and reliability will be acceptable in clinical practice. Artificial intelligence has shown successes in general image analysis. In order to facilitate the studies of how Artificial Intelligence can be applied in automatic segmentation of brain tumor MR images, two automatic segmentation software, Bratumia and DeepNeuro's SegmentGBM are employed. BraTumia adopts a Machine Learning approach (with conditional random field for spatial regularization) while DeepNeuro's SegmentGBM adopts Deep Learning. During the thesis, experiments have been conducted to investigate how each component of the pre-processing and segmentation pipeline affects the accuracy of the overall segmentation.

Relatori: Anna Filomena Carbone, Diego Garbossa
Anno accademico: 2019/20
Tipo di pubblicazione: Elettronica
Numero di pagine: 118
Informazioni aggiuntive: Tesi secretata. Fulltext non presente
Soggetti:
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-32 - INGEGNERIA INFORMATICA
Aziende collaboratrici: NON SPECIFICATO
URI: http://webthesis.biblio.polito.it/id/eprint/15301
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