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Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging Using Model Ensembling and Super-resolution

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 12963))

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Abstract

Brain tumor segmentation in MRI offers critical quantitative imaging data to characterize and improve prognosis. The International Brain Tumor Segmentation (BraTS) Challenge provides a unique opportunity to encourage machine learning solutions to address this challenging task. This year, the 10th edition of BraTS collected a multi-institutional multi-parametric MRI dataset of 2040 cases with typical heterogeneity in large multi-domain imaging datasets. In this paper we present a strategy ensembling four parallelly-trained models to increase the stability and performance of our neural network-based tumor segmentation. Particularly, image intensity normalization and multi-parametric MRI super-resolution techniques are used in ensembled pipelines. The evaluation of our solution on 570 unseen testing cases resulted in Dice scores of 86.28, 87.12 and 92.10, and Hausdorff distance of 14.36, 17.48 and 5.37 mm for the enhancing tumor, tumor core and whole tumor, respectively.

Z. Jiang and C. Zhao—These authors contributed equally.

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Notes

  1. 1.

    https://monai.io.

  2. 2.

    https://developer.nvidia.com/clara.

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Acknowledgements

The authors would like to thank Holger Roth from NVIDIA, Bethesda, MD, USA and Brendan Wang from Princeton University, NJ, USA for their contributions to this work. Partial support for this work was also provided by National Cancer Institute award UG3 CA236536.

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Authors and Affiliations

  1. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, USA

    Zhifan Jiang, Xinyang Liu & Marius George Linguraru

  2. NVIDIA, Bethesda, MD, USA

    Can Zhao

  3. School of Medicine and Health Sciences, George Washington University, Washington, DC, USA

    Marius George Linguraru

Authors
  1. Zhifan Jiang
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  2. Can Zhao
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  3. Xinyang Liu
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  4. Marius George Linguraru
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Correspondence to Zhifan Jiang .

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Editors and Affiliations

  1. Sano Centre for Computational Personaliz, Kraków, Poland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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Jiang, Z., Zhao, C., Liu, X., Linguraru, M.G. (2022). Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging Using Model Ensembling and Super-resolution. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_12

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  • DOI: https://doi.org/10.1007/978-3-031-09002-8_12

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