Abstract
Our contribution to the BraTS 2019 challenge consisted of a deep learning based approach for segmentation of brain tumours from MR images using cross validation ensembles of 2D-UNet models. Furthermore, different approaches for the prediction of patient survival time using clinical as well as imaging features were investigated. A simple linear regression model using patient age and tumour volumes outperformed more elaborate approaches like convolutional neural networks or radiomics-based analysis with an accuracy of 0.55 on the validation cohort and 0.51 on the test cohort.
S. Starke and C. Eckert—Shared first authorship.
S. Löck and S. Leger—Shared senior authorship.
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The author SLe is supported by the Federal Ministry of Education and Research (BMBF-13GW0211D).
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Starke, S., Eckert, C., Zwanenburg, A., Speidel, S., Löck, S., Leger, S. (2020). An Integrative Analysis of Image Segmentation and Survival of Brain Tumour Patients. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11992. Springer, Cham. https://doi.org/10.1007/978-3-030-46640-4_35
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