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 …
Originalsprache | englisch |
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Titel | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers |
Seiten | 6724-6729 |
Seitenumfang | 6 |
ISBN (elektronisch) | 978-1-5386-1311-5 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2019 |
Veranstaltung | 41st Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: EMBC 2019 - CityCube, Berlin, Deutschland Dauer: 23 Juli 2019 → 27 Juli 2019 Konferenznummer: 41 https://embc.embs.org/2019/ |
Konferenz
Konferenz | 41st Annual International Conferences of the IEEE Engineering in Medicine and Biology Society |
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Kurztitel | IEEE EMBC 2019 |
Land/Gebiet | Deutschland |
Ort | Berlin |
Zeitraum | 23/07/19 → 27/07/19 |
Internetadresse |