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MICCAI Workshop on Deep Generative Models

MICCAI Workshop on Data Augmentation, Labelling, and Imperfections

DGM4MICCAI 2021, DALI 2021: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections pp 134–141Cite as

Cross-Domain Landmarks Detection in Mitral Regurgitation

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 13003)

Abstract

Mitral regurgitation (MR) is a frequent indication for valve surgery. One of its treatments, mitral valve performed with endoscopic video recordings, is a complex minimally invasive procedure which is facing the problem of data availability and data privacy. Therefore, the simulation cases are widely used to form surgery training and planning. However, the cross-domain gap may affect the performance significantly as Deep Learning methods rely heavily on data. We propose to develop an algorithm to reduce the domain gap between simulation and intra-operative cases. The task is to learn the distance and location information of the points and predict a series of 2D landmarks’ location, the coordinates of the landmark were both marked on real and simulate dataset by the AdaptOR Challenge organizer. Our work has merged the data from both domains by using a relation heatmap generation algorithm, which can generate a relation key point heatmap based on the distance measurement of landmarks and explicitly represent the geometric relation between landmarks. The MSE loss function is used to minimize the error between the ground-truth and predicted heatmaps. We test our methods on a challenge dataset, in which the model has achieved a good F1 score of 66.19%.

Keywords

  • Landmark detection
  • Mitral regurgitation
  • Deep learning

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  • DOI: 10.1007/978-3-030-88210-5_12
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Authors and Affiliations

  1. Department of Computer Science at School of Informatics, Xiamen University, Xiamen, China

    Jiacheng Wang, Haojie Wang, Ruochen Mu & Liansheng Wang

Authors
  1. Jiacheng Wang
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  2. Haojie Wang
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  3. Ruochen Mu
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  4. Liansheng Wang
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Corresponding author

Correspondence to Liansheng Wang .

Editor information

Editors and Affiliations

  1. Universitätsklinikum Heidelberg, Heidelberg, Germany

    Sandy Engelhardt

  2. Istanbul Technical University, Istanbul, Turkey

    Ilkay Oksuz

  3. The University of Texas at Arlington, Arlington, TX, USA

    Dajiang Zhu

  4. University of Hong Kong, Hong Kong, Hong Kong

    Yixuan Yuan

  5. TU Darmstadt, Darmstadt, Germany

    Anirban Mukhopadhyay

  6. University of Minnesota, Minneapolis, MN, USA

    Nicholas Heller

  7. Pennsylvania State University, University Park, PA, USA

    Sharon Xiaolei Huang

  8. University of Houston, Houston, TX, USA

    Hien Nguyen

  9. University of Bern, Bern, Switzerland

    Raphael Sznitman

  10. Johns Hopkins University, Baltimore, MD, USA

    Yuan Xue

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Wang, J., Wang, H., Mu, R., Wang, L. (2021). Cross-Domain Landmarks Detection in Mitral Regurgitation. In: , et al. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. DGM4MICCAI DALI 2021 2021. Lecture Notes in Computer Science(), vol 13003. Springer, Cham. https://doi.org/10.1007/978-3-030-88210-5_12

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  • DOI: https://doi.org/10.1007/978-3-030-88210-5_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88209-9

  • Online ISBN: 978-3-030-88210-5

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