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Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction Based on Semi-supervised Contrastive Learning

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Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14092))

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Abstract

Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers, as well as to complement the missing modalities. In this challenge, we proposed an unsupervised domain adaptation framework for cross-modality vestibular schwannoma (VS) and cochlea segmentation and Koos grade prediction. We learn the shared representation from both ceT1 and hrT2 images and recover another modality from the latent representation, and we also utilize proxy tasks of VS segmentation and brain parcellation to restrict the consistency of image structures in domain adaptation. After generating missing modalities, the nnU-Net model is utilized for VS and cochlea segmentation, while a semi-supervised contrastive learning pre-train approach is employed to improve the model performance for Koos grade prediction. On CrossMoDA validation phase Leaderboard, our method received rank 4 in task1 with a mean Dice score of 0.8394 and rank 2 in task2 with Macro-Average Mean Square Error of 0.3941. Our code is available athttps://github.com/fiy2W/cmda2022.superpolymerization.

L. Han and Y. Huang—Contributed equally to this work.

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Acknowledgement

Luyi Han was funded by Chinese Scholarship Council (CSC) scholarship. This work was supported by the National Natural Science Foundation of China under Grant No. 62101365 and the startup foundation of Nanjing University of Information Science and Technology.

Author information

Authors and Affiliations

  1. Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525, Nijmegen, GA, The Netherlands

    Luyi Han & Ritse Mann

  2. Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, Macao, Special Administrative Region of China

    Tao Tan

  3. Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066, Amsterdam, CX, The Netherlands

    Luyi Han, Tao Tan & Ritse Mann

  4. School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yunzhi Huang

Authors
  1. Luyi Han

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  2. Yunzhi Huang

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  3. Tao Tan

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  4. Ritse Mann

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Corresponding author

Correspondence toTao Tan.

Editor information

Editors and Affiliations

  1. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

  2. Sano, Center for Computational Personalised Medicine, Kraków, Poland

    Alessandro Crimi

  3. University of Pennsylvania, Philadelphia, PA, USA

    Ujjwal Baid

  4. Sano, Center for Computational Personalised Medicine, Kraków, Poland

    Sylwia Malec

  5. Sano, Center for Computational Personalised Medicine, Kraków, Poland

    Monika Pytlarz

  6. University of Pennsylvania, Philadelphia, PA, USA

    Bhakti Baheti

  7. German Cancer Research Center, Heidelberg, Germany

    Maximilian Zenk

  8. Harvard Medical School, Boston, MA, USA

    Reuben Dorent

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Han, L., Huang, Y., Tan, T., Mann, R. (2023). Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction Based on Semi-supervised Contrastive Learning. In: Bakas, S.,et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2022. Lecture Notes in Computer Science, vol 14092. Springer, Cham. https://doi.org/10.1007/978-3-031-44153-0_5

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