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Segmentation and visualization of left atrium through a unified deep learning framework

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Left atrium segmentation and visualization serve as a fundamental and crucial role in clinical analysis and understanding of atrial fibrillation. However, most of the existing methods are directly transmitting information, which may cause redundant information to be passed to affect segmentation performance. Moreover, they did not further consider atrial visualization after segmentation, which leads to a lack of understanding of the essential atrial anatomy.

Methods

We propose a novel unified deep learning framework for left atrium segmentation and visualization simultaneously. At first, a novel dual-path module is used to enhance the expressiveness of cardiac image representation. Then a multi-scale context-aware module is designed to effectively handle complex appearance and shape variations of the left atrium and associated pulmonary veins. The generated multi-scale features are feed to gated bidirectional message passing module to remove irrelevant information and extract discriminative features. Finally, the features after message passing are efficiently combined via a deep supervision mechanism to produce the final segmentation result and reconstruct 3D volumes.

Results

Our approach primarily against the 2018 left atrium segmentation challenge dataset, which consists of 100 3D gadolinium-enhanced magnetic resonance images. Our method achieves an average dice of 0.936 in segmenting the left atrium via fivefold cross-validation, which outperforms state-of-the-art methods.

Conclusions

The performance demonstrates the effectiveness and advantages of our network for the left atrium segmentation and visualization. Therefore, our proposed network could potentially improve the clinical diagnosis and treatment of atrial fibrillation.

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Acknowledgements

We thank all the anonymous reviewers for their valuable comments and constructive suggestions, which were helpful for improving the quality of the paper. This work was supported in part by the National Science Foundation of China under Grant 61673020, the Anhui Provincial Natural Science Foundation under Grand 1708085QF143.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, China

    Xiuquan Du & Yanping Zhang

  2. School of Computer Science and Technology, Anhui University, Hefei, China

    Xiuquan Du, Susu Yin, Renjun Tang, Yueguo Liu, Yuhui Song & Yanping Zhang

  3. Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China

    Heng Liu

  4. Department of Medical Imaging, Western University, London, Canada

    Shuo Li

Authors
  1. Xiuquan Du
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  2. Susu Yin
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  3. Renjun Tang
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  4. Yueguo Liu
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  5. Yuhui Song
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  6. Yanping Zhang
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  7. Heng Liu
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  8. Shuo Li
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Correspondence to Xiuquan Du.

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All procedures performed in our study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee.

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Cite this article

Du, X., Yin, S., Tang, R. et al. Segmentation and visualization of left atrium through a unified deep learning framework. Int J CARS 15, 589–600 (2020). https://doi.org/10.1007/s11548-020-02128-9

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  • DOI: https://doi.org/10.1007/s11548-020-02128-9

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