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Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge

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Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition (SEGA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14539))

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Abstract

Aorta provides the main blood supply of the body. Screening of aorta with imaging helps for early aortic disease detection and monitoring. In this work, we describe our solution to the Segmentation of the Aorta (SEG.A.23 (https://multicenteraorta.grand-challenge.org/multicenteraorta/)) from 3D CT challenge. We use automated segmentation method Auto3DSeg (https://monai.io/apps/auto3dseg) available in MONAI (https://github.com/Project-MONAI/MONAI). Our solution achieves an average Dice score of 0.920 and 95th percentile of the Hausdorff Distance (HD95) of 6.013, which ranks first and wins the SEG.A. 2023 challenge.

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Notes

  1. 1.

    https://docs.monai.io/en/stable/networks.html.

  2. 2.

    https://docs.monai.io/en/latest/_modules/monai/inferers/inferer.html.

  3. 3.

    https://multicenteraorta.grand-challenge.org/main-task-final-leaderboard/.

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Authors and Affiliations

  1. NVIDIA, Santa Clara, USA

    Andriy Myronenko, Dong Yang, Yufan He & Daguang Xu

Authors
  1. Andriy Myronenko
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  2. Dong Yang
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  3. Yufan He
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  4. Daguang Xu
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Corresponding author

Correspondence to Andriy Myronenko .

Editor information

Editors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Antonio Pepe

  2. Graz University of Technology, Graz, Austria

    Gian Marco Melito

  3. Essen University Hospital, Essen, Germany

    Jan Egger

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Myronenko, A., Yang, D., He, Y., Xu, D. (2024). Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge. In: Pepe, A., Melito, G.M., Egger, J. (eds) Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition. SEGA 2023. Lecture Notes in Computer Science, vol 14539. Springer, Cham. https://doi.org/10.1007/978-3-031-53241-2_2

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  • DOI: https://doi.org/10.1007/978-3-031-53241-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53240-5

  • Online ISBN: 978-3-031-53241-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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