Using Synthetic Training Data for Deep Learning-Based GBM Segmentation

Lydia Lindner, Dominik Narnhofer, Maximilian Weber, Christina Gsaxner, Jan Egger, Malgorzata Kolodziej

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

In this work, fully automatic binary segmentation of GBMs (glioblastoma multiforme) in 2D magnetic resonance images is presented using a convolutional neural network trained exclusively on synthetic data. The precise segmentation of brain tumors is one of the most complex and challenging tasks in clinical practice and is usually done manually by radiologists or physicians. However, manual delineations are time-consuming, subjective and in general not reproducible. Hence, more advanced automated segmentation techniques are in great demand. After deep learning methods already successfully demonstrated their practical usefulness in other domains, they are now also attracting increasing interest in the field of medical image processing. Using fully convolutional neural networks for medical image segmentation provides considerable advantages, as it is a reliable, fast and objective technique. In the medical …
Originalspracheenglisch
Titel2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
Seiten6724-6729
Seitenumfang6
ISBN (elektronisch)978-1-5386-1311-5
DOIs
PublikationsstatusVeröffentlicht - 2019
Veranstaltung41st Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: EMBC 2019 - CityCube, Berlin, Deutschland
Dauer: 23 Juli 201927 Juli 2019
Konferenznummer: 41
https://embc.embs.org/2019/

Konferenz

Konferenz41st Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
KurztitelIEEE EMBC 2019
Land/GebietDeutschland
OrtBerlin
Zeitraum23/07/1927/07/19
Internetadresse

Fingerprint

Untersuchen Sie die Forschungsthemen von „Using Synthetic Training Data for Deep Learning-Based GBM Segmentation“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren