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Generative adversarial networks for automated hippocampus segmentation : development of an artificial neural network and integration in a generative adversarial network scheme to improve the segmentation of the hippocampus and its substructures

  • Alzheimer’s Disease affects millions of people worldwide, but till today, the gold standard for definitive diagnosis of this disease is a biopsy. Nevertheless, with the progress of the disease, a volume loss in the Hippocampus can be observed. Therefore, good segmentation methods are crucial to facilitate quantification of this loss. The focus of this work is on the development of a Machine Learning algorithm, more precisely a Generative Adversarial Network, for the automated segmentation of the human Hippocampus and its substructures in Magnetic Resonance Images. In particular, the task is to determine if the integration of a pre-trained network that generates segmentations into a Generative Adversarial Network scheme can improve generated segmentations. In this context, a segmentation network in form of a U-net corresponds to the generator. The discriminator is developed separately and merged in a second step with the generator for combined training. With a literature review regarding the automated segmentation of the Hippocampus, current methods in this field and their medical and technological basics were identified. The datasets were preprocessed to make them suitable for the use in a neural network. In the training process, the generator was trained first until convergence. Then, the Generative Adversarial Network including the pre-trained generator was trained. The outcomes were evaluated via cross-validation in two different datasets (Kulaga-Yoskovitz and Winterburn). The Generative Adversarial Network scheme was tested regarding different architectural and training aspects, including the usage of skip-connections and a combined loss function. The best results were achieved in the Kulaga-Yoskovitz dataset with a Dice coefficient of 90.84 % after the combined training of generator and discriminator with a joined loss function. This improves the current state of the art method in the same task and dataset with a Dice index of 88.79 % by Romero [Rom17]. Except of two cases in the Winterburn dataset, the proposed combined method could always improve the Dice results after the training of only the generator, even though only by a small amount.

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Author:Tanja Eichner
Referee:Rolf Bendl, Christoph Maier
Advisor:José Vicente Manjón Herrera
Document Type:Bachelor Thesis
Year of Completion:2019
Publishing Institution:Hochschule Heilbronn
Granting Institution:Hochschule Heilbronn
Date of final exam:2019/02/20
Release Date:2019/02/28
Tag:Alzheimer; Hippocampus
GND Keyword:Maschinelles Lernen; Neuronales Netz
Pagenumber:VIII, 45 Seiten
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft
Access Right:Frei zugänglich
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung