Towards uncertainty-assisted brain tumor segmentation and survival prediction

Jungo, Alain; McKinley, Richard; Meier, Raphael; Knecht, Urspeter; Vera, Luis; Pérez-Beteta, Julián; Molina-García, David; Pérez-García, Víctor M.; Wiest, Roland; Reyes, Mauricio (2018). Towards uncertainty-assisted brain tumor segmentation and survival prediction. In: International Conference On Medical Image Computing & Computer Assisted Intervention. 10.1007/978-3-319-75238-9_40

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Uncertainty measures of medical image analysis technologies, such as deep learning, are expected to facilitate their clinical acceptance and synergies with human expertise. Therefore, we propose a full-resolution residual convolutional neural network (FRRN) for brain tumor segmentation and examine the principle of Monte Carlo (MC) Dropout for uncertainty quantification by focusing on the Dropout position and rate. We further feed the resulting brain tumor segmentation into a survival prediction model, which is built on age and a subset of 26 image-derived geometrical features such as volume, volume ratios, surface, surface irregularity and statistics of the enhancing tumor rim width. The results show comparable segmentation performance between MC Dropout models and a standard weight scaling Dropout model. A qualitative evaluation further suggests that informative uncertainty can be obtained by applying MC Dropout after each convolution layer. For survival prediction, results suggest only using few features besides age. In the BraTS17 challenge, our method achieved the 2nd place in the survival task and completed the segmentation task in the 3rd best-performing cluster of statistically different approaches.

Item Type:

Conference or Workshop Item (Paper)


04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB [discontinued]
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Jungo, Alain, McKinley, Richard Iain, Meier, Raphael, Knecht, Urspeter, Wiest, Roland Gerhard Rudi, Reyes, Mauricio


500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health




Lecture Notes in Computer Science




Alain Jungo

Date Deposited:

17 Sep 2019 14:38

Last Modified:

02 Mar 2023 23:32

Publisher DOI:


Additional Information:

International MICCAI Brainlesion Workshop




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