TuMore: generation of synthetic brain tumor MRI data for deep learning based segmentation approaches

Lydia Lindner; Birgit Pfarrkirchner; Christina Gsaxner; Dieter Schmalstieg; Jan Egger

doi:10.1117/12.2315704

6 March 2018 TuMore: generation of synthetic brain tumor MRI data for deep learning based segmentation approaches

Lydia Lindner, Birgit Pfarrkirchner, Christina Gsaxner, Dieter Schmalstieg, Jan Egger

Proceedings Volume 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications; 105791C (2018) https://doi.org/10.1117/12.2315704
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Accurate segmentation and measurement of brain tumors plays an important role in clinical practice and research, as it is critical for treatment planning and monitoring of tumor growth. However, brain tumor segmentation is one of the most challenging tasks in medical image analysis. Since manual segmentations are subjective, time consuming and neither accurate nor reliable, there exists a need for objective, robust and fast automated segmentation methods that provide competitive performance. Therefore, deep learning based approaches are gaining interest in the field of medical image segmentation. When the training data set is large enough, deep learning approaches can be extremely effective, but in domains like medicine, only limited data is available in the majority of cases. Due to this reason, we propose a method that allows to create a large dataset of brain MRI (Magnetic Resonance Imaging) images containing synthetic brain tumors - glioblastomas more specifically - and the corresponding ground truth, that can be subsequently used to train deep neural networks.

Citation Download Citation

Lydia Lindner, Birgit Pfarrkirchner, Christina Gsaxner, Dieter Schmalstieg, and Jan Egger "TuMore: generation of synthetic brain tumor MRI data for deep learning based segmentation approaches", Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105791C (6 March 2018); https://doi.org/10.1117/12.2315704

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