Application of neural networks for tissue segmentation in biomedical patient images

Ovaj rad posvećen je primjeni strojnog učenja u području biomedcinske segmentacije tkiva ljudskog mozga; prvenstveno se fokusira na segmentaciju ventrikula koje se mogu primijetiti na CT snimkama mozga. Opisan je način prikupljanja podataka, način na koji se ti podaci pripremaju za korištenje te sama umjetna neuronska mreža iskorištena za postupak segmentacije. Cilj rada je dokazati da se primjenom konvolucijske umjetne neuronske mreže može postići konkurentna točnost kod segmentacije ventrikula u mozgu, te postaviti temelje za eventualno izvođenje segmentacije drugih tkiva u mozgu.This diploma work is devoted to the application of machine learning in the field of biomedical tissue segmentation of the human brain with a primary focus on segmentation of the ventricles that can be seen on CT brain images. The paper contains a description of several thing: how the data is collected, how is this data prepared and what artificial neural network is used for the segmentation process. The aim of the paper is to prove that it is possible to achieve competitive accuracy in the segmentation of the ventricles in the brain using a convolutional artificial neural network, and to lay the groundwork for the eventual segmentation of other tissues in the brain