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International MICCAI Brainlesion Workshop

BrainLes 2018: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 367–377Cite as

MixNet: Multi-modality Mix Network for Brain Segmentation

  • 2018 Accesses

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11383)

Abstract

Automated brain structure segmentation is important to many clinical quantitative analysis and diagnoses. In this work, we introduce MixNet, a 2D semantic-wise deep convolutional neural network to segment brain structure in multi-modality MRI images. The network is composed of our modified deep residual learning units. In the unit, we replace the traditional convolution layer with the dilated convolutional layer, which avoids the use of pooling layers and deconvolutional layers, reducing the number of network parameters. Final predictions are made by aggregating information from multiple scales and modalities. A pyramid pooling module is used to capture spatial information of the anatomical structures at the output end. In addition, we test three architectures (MixNetv1, MixNetv2 and MixNetv3) which fuse the modalities differently to see the effect on the results. Our network achieves the state-of-the-art performance. MixNetv2 was submitted to the MRBrainS challenge at MICCAI 2018 and won the 3rd place in the 3-label task. On the MRBrainS2018 dataset, which includes subjects with a variety of pathologies, the overall DSC (Dice Coefficient) of 84.7% (gray matter), 87.3% (white matter) and 83.4% (cerebrospinal fluid) were obtained with only 7 subjects as training data.

Keywords

  • Brain segmentation
  • CNN
  • Multi-modality

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  • DOI: 10.1007/978-3-030-11723-8_37
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Authors and Affiliations

  1. Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

    Long Chen & Dorit Merhof

Authors
  1. Long Chen
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  2. Dorit Merhof
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Corresponding author

Correspondence to Dorit Merhof .

Editor information

Editors and Affiliations

  1. University Hospital of Zurich, Zürich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

  3. University Medical Center Utrecht, Utrecht, The Netherlands

    Hugo Kuijf

  4. National Cancer Institute, Bethesda, MD, USA

    Farahani Keyvan

  5. University of Bern, Bern, Switzerland

    Mauricio Reyes

  6. Erasmus University Medical Center, Rotterdam, The Netherlands

    Theo van Walsum

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Chen, L., Merhof, D. (2019). MixNet: Multi-modality Mix Network for Brain Segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science(), vol 11383. Springer, Cham. https://doi.org/10.1007/978-3-030-11723-8_37

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  • DOI: https://doi.org/10.1007/978-3-030-11723-8_37

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