Using Synthetic Training Data for Deep Learning-Based GBM Segmentation

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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 …
Original languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherInstitute of Electrical and Electronics Engineers
Pages6724-6729
Number of pages6
ISBN (Electronic)978-1-5386-1311-5
DOIs
Publication statusPublished - 2019
Event41st International Engineering in Medicine and Biology Conference 2019 - Berlin, Germany
Duration: 23 Jul 201927 Jul 2019
Conference number: 41
https://embc.embs.org/2019/

Conference

Conference41st International Engineering in Medicine and Biology Conference 2019
Abbreviated titleIEEE EMBC 2019
CountryGermany
CityBerlin
Period23/07/1927/07/19
Internet address

Fingerprint

Medical image processing
Neural networks
Magnetic resonance
Image segmentation
Tumors
Brain
Deep learning

Cite this

Lindner, L., Narnhofer, D., Weber, M., Gsaxner, C., Egger, J., & Kolodziej, M. (2019). Using Synthetic Training Data for Deep Learning-Based GBM Segmentation. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6724-6729). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2019.8856297

Using Synthetic Training Data for Deep Learning-Based GBM Segmentation. / Lindner, Lydia; Narnhofer, Dominik; Weber, Maximilian; Gsaxner, Christina; Egger, Jan; Kolodziej, Malgorzata.

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers, 2019. p. 6724-6729.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Lindner, L, Narnhofer, D, Weber, M, Gsaxner, C, Egger, J & Kolodziej, M 2019, Using Synthetic Training Data for Deep Learning-Based GBM Segmentation. in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers, pp. 6724-6729, 41st International Engineering in Medicine and Biology Conference 2019, Berlin, Germany, 23/07/19. https://doi.org/10.1109/EMBC.2019.8856297
Lindner L, Narnhofer D, Weber M, Gsaxner C, Egger J, Kolodziej M. Using Synthetic Training Data for Deep Learning-Based GBM Segmentation. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers. 2019. p. 6724-6729 https://doi.org/10.1109/EMBC.2019.8856297
Lindner, Lydia ; Narnhofer, Dominik ; Weber, Maximilian ; Gsaxner, Christina ; Egger, Jan ; Kolodziej, Malgorzata. / Using Synthetic Training Data for Deep Learning-Based GBM Segmentation. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers, 2019. pp. 6724-6729
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