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MRI Brain Tumor Segmentation and Uncertainty Estimation Using 3D-UNet Architectures

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12658))

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Abstract

Automation of brain tumor segmentation in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs) have shown improved results in the task. However, high memory consumption is still a problem in 3D-CNNs. Moreover, most methods do not include uncertainty information, which is especially critical in medical diagnosis. This work studies 3D encoder-decoder architectures trained with patch-based techniques to reduce memory consumption and decrease the effect of unbalanced data. The different trained models are then used to create an ensemble that leverages the properties of each model, thus increasing the performance. We also introduce voxel-wise uncertainty information, both epistemic and aleatoric using test-time dropout (TTD) and data-augmentation (TTA) respectively. In addition, a hybrid approach is proposed that helps increase the accuracy of the segmentation. The model and uncertainty estimation measurements proposed in this work have been used in the BraTS’20 Challenge for task 1 and 3 regarding tumor segmentation and uncertainty estimation.

V. Vilaplana—This work has been partially supported by the project MALEGRA TEC2016-75976-R financed by the Spanish Ministerio de Economía y Competitividad.

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Notes

  1. 1.

    Github repository: https://github.com/imatge-upc/mri-braintumor-segmentation.

  2. 2.

    The Image and Video Processing Group (GPI) is a research group of the Signal Theory and Communications Department, Universitat Politècnica de Catalunya.

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Authors and Affiliations

  1. Signal Theory and Communications Department, Universitat Politècnica de Catalunya. BarcelonaTech, Barcelona, Spain

    Laura Mora Ballestar & Veronica Vilaplana

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  1. Laura Mora Ballestar
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  2. Veronica Vilaplana
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Correspondence to Veronica Vilaplana .

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Editors and Affiliations

  1. University of Zurich, Zurich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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Ballestar, L.M., Vilaplana, V. (2021). MRI Brain Tumor Segmentation and Uncertainty Estimation Using 3D-UNet Architectures. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_34

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  • DOI: https://doi.org/10.1007/978-3-030-72084-1_34

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