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 Konferenzband


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 …
Titel2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
ISBN (elektronisch)978-1-5386-1311-5
PublikationsstatusVeröffentlicht - 2019
Veranstaltung41st International Engineering in Medicine and Biology Conference 2019 - Berlin, Deutschland
Dauer: 23 Jul 201927 Jul 2019
Konferenznummer: 41


Konferenz41st International Engineering in Medicine and Biology Conference 2019
KurztitelIEEE EMBC 2019

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

Dieses zitieren