Information
- Publication Type: Bachelor Thesis
- Workgroup(s)/Project(s):
- Date: August 2017
- Date (Start): 2016
- Date (End): 2017
- Matrikelnummer: 1427881
- First Supervisor: Werner Purgathofer
Abstract
The aim of this thesis is to describe deep learning approaches for vessel segmentation in 2 and 3-dimensional biomedical images and the results achieved from these approaches on specific sets of data. The first chapter introduces the objective of this thesis, describes the data, which was used for the training, gives a short overview of machine learning and covers some theoretical aspects of artificial neural networks and especially of convolutional neural networks. The second chapter describes methods that were used for achieving the segmentation in 2 and 3 dimensions, like preprocessing of the images, algorithmic approaches, and general project set-up. The third and final chapter focuses on the results of methods described in chapter 2, contains personal advice for future approaches for improving the algorithm’s results and discusses the results. The thesis provides the theory, code snippets for the most fundamental part of the algorithms’ implementations and shows graphical, as well as numerical results of the approaches.Additional Files and Images
Weblinks
No further information available.BibTeX
@bachelorsthesis{Zusag-2017-Bach, title = "Deep Learning Architectures for vessel Segmentation in 2D and 3D Biomedical Images", author = "Mario Zusag", year = "2017", abstract = "The aim of this thesis is to describe deep learning approaches for vessel segmentation in 2 and 3-dimensional biomedical images and the results achieved from these approaches on specific sets of data. The first chapter introduces the objective of this thesis, describes the data, which was used for the training, gives a short overview of machine learning and covers some theoretical aspects of artificial neural networks and especially of convolutional neural networks. The second chapter describes methods that were used for achieving the segmentation in 2 and 3 dimensions, like preprocessing of the images, algorithmic approaches, and general project set-up. The third and final chapter focuses on the results of methods described in chapter 2, contains personal advice for future approaches for improving the algorithm’s results and discusses the results. The thesis provides the theory, code snippets for the most fundamental part of the algorithms’ implementations and shows graphical, as well as numerical results of the approaches.", month = aug, address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", school = "Institute of Computer Graphics and Algorithms, Vienna University of Technology ", URL = "https://www.cg.tuwien.ac.at/research/publications/2017/Zusag-2017-Bach/", }