Segmentation of brain tumors and patient survival prediction: Methods for the braTS 2018 challenge
Weninger L, Rippel O, Koppers S, Merhof D (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Verlag
Book Volume: 11384 LNCS
Pages Range: 3-12
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030117252
DOI: 10.1007/978-3-030-11726-9_1
Abstract
Brain tumor localization and segmentation is an important step in the treatment of brain tumor patients. It is the base for later clinical steps, e.g., a possible resection of the tumor. Hence, an automatic segmentation algorithm would be preferable, as it does not suffer from inter-rater variability. On top, results could be available immediately after the brain imaging procedure. Using this automatic tumor segmentation, it could also be possible to predict the survival of patients. The BraTS 2018 challenge consists of these two tasks: tumor segmentation in 3D-MRI images of brain tumor patients and survival prediction based on these images. For the tumor segmentation, we utilize a two-step approach: First, the tumor is located using a 3D U-net. Second, another 3D U-net – more complex, but with a smaller output size – detects subtle differences in the tumor volume, i.e., it segments the located tumor into tumor core, enhanced tumor, and peritumoral edema. The survival prediction of the patients is done with a rather simple, yet accurate algorithm which outperformed other tested approaches on the train set when thoroughly cross-validated. This finding is consistent with our performance on the test set - we achieved 3rd place in the survival prediction task of the BraTS Challenge 2018.
Involved external institutions
Germany (DE)How to cite
APA:
Weninger, L., Rippel, O., Koppers, S., & Merhof, D. (2019). Segmentation of brain tumors and patient survival prediction: Methods for the braTS 2018 challenge. In Mauricio Reyes, Hugo Kuijf, Theo van Walsum, Farahani Keyvan, Alessandro Crimi, Spyridon Bakas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 3-12). Granada, ESP: Springer Verlag.
MLA:
Weninger, Leon, et al. "Segmentation of brain tumors and patient survival prediction: Methods for the braTS 2018 challenge." Proceedings of the 4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018, Granada, ESP Ed. Mauricio Reyes, Hugo Kuijf, Theo van Walsum, Farahani Keyvan, Alessandro Crimi, Spyridon Bakas, Springer Verlag, 2019. 3-12.
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