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Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging

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Brain Tumor Segmentation, and Cross-Modality Domain Adaptation for Medical Image Segmentation (crossMoDA 2023, BraTS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14669))

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Abstract

Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This analysis provides the potential to impact clinical decision-making processes, including diagnosis and prognosis. In 2023, the well-established Brain Tumor Segmentation (BraTS) challenge presented a substantial expansion with eight tasks and 4,500 brain tumor cases. In this paper, we present a deep learning-based ensemble strategy that is evaluated for newly included tumor cases in three tasks: pediatric brain tumors (PED), intracranial meningioma (MEN), and brain metastases (MET). In particular, we ensemble outputs from state-of-the-art nnU-Net and Swin UNETR models on a region-wise basis. Furthermore, we implemented a targeted post-processing strategy based on a cross-validated threshold search to improve the segmentation results for tumor sub-regions. The evaluation of our proposed method on unseen test cases for the three tasks resulted in lesion-wise Dice scores for PED: 0.653, 0.809, 0.826; MEN: 0.876, 0.867, 0.849; and MET: 0.555, 0.6, 0.58; for the enhancing tumor, tumor core, and whole tumor, respectively. Our method was ranked first for PED, third for MEN, and fourth for MET, respectively.

D. Capellán-Martín, Z. Jiang and A. Parida—These authors contributed equally.

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Notes

  1. 1.

    https://monai.io.

  2. 2.

    optuna.readthedocs.io/.

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Acknowledgements

Partial support for this work was provided by the National Cancer Institute (UG3 CA236536) and by the Spanish Ministerio de Ciencia e Innovación, the Agencia Estatal de Investigación and NextGenerationEU funds, under grants PDC2022-133865-I00 and PID2022-141493OB-I00. The authors gratefully acknowledge the Universidad Politécnica de Madrid (www.upm.es) for providing computing resources on Magerit Supercomputer.

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Authors and Affiliations

  1. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, 20010, USA

    Daniel Capellán-Martín, Zhifan Jiang, Abhijeet Parida, Xinyang Liu, Van Lam, Hareem Nisar, Austin Tapp, Sarah Elsharkawi, Syed Muhammad Anwar & Marius George Linguraru

  2. Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid 28040, Spain and CIBER-BBN, ISCIII, Madrid, Spain

    Daniel Capellán-Martín & María J. Ledesma-Carbayo

  3. Princeton University, Princeton, NJ, 08544, USA

    Sarah Elsharkawi

  4. School of Medicine and Health Sciences, George Washington University, Washington, DC, 20052, USA

    Syed Muhammad Anwar & Marius George Linguraru

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  1. Daniel Capellán-Martín
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Correspondence to Marius George Linguraru .

Editor information

Editors and Affiliations

  1. Indiana University School of Medicine, Indianapolis, IN, USA

    Ujjwal Baid

  2. Harvard Medical School, Boston, MA, USA

    Reuben Dorent

  3. Indiana University School of Medicine, Indianapolis, IN, USA

    Sylwia Malec

  4. Sano – Centre for Computational Personalised Medicine International Research Foundation, Kraków, Poland

    Monika Pytlarz

  5. Eindhoven University of Technology, Eindhoven, The Netherlands

    Ruisheng Su

  6. King's College London, London, UK

    Navodini Wijethilake

  7. Indiana University School of Medicine, Indianapolis, IN, USA

    Spyridon Bakas

  8. Sano – Centre for Computational Personalised Medicine International Research Foundation, Kraków, Poland

    Alessandro Crimi

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Capellán-Martín, D. et al. (2024). Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging. In: Baid, U., et al. Brain Tumor Segmentation, and Cross-Modality Domain Adaptation for Medical Image Segmentation. crossMoDA BraTS 2023 2023. Lecture Notes in Computer Science, vol 14669. Springer, Cham. https://doi.org/10.1007/978-3-031-76163-8_20

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  • DOI: https://doi.org/10.1007/978-3-031-76163-8_20

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