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Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (MICCAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12264))

Abstract

We propose an end-to-end deep neural network (DNN) which can simultaneously segment the left atrial (LA) cavity and quantify LA scars. The framework incorporates the continuous spatial information of the target by introducing a spatially encoded (SE) loss based on the distance transform map. Compared to conventional binary label based loss, the proposed SE loss can reduce noisy patches in the resulting segmentation, which is commonly seen for deep learning-based methods. To fully utilize the inherent spatial relationship between LA and LA scars, we further propose a shape attention (SA) mechanism through an explicit surface projection to build an end-to-end-trainable model. Specifically, the SA scheme is embedded into a two-task network to perform the joint LA segmentation and scar quantification. Moreover, the proposed method can alleviate the severe class-imbalance problem when detecting small and discrete targets like scars. We evaluated the proposed framework on 60 LGE MRI data from the MICCAI2018 LA challenge. For LA segmentation, the proposed method reduced the mean Hausdorff distance from 36.4 mm to 20.0 mm compared to the 3D basic U-Net using the binary cross-entropy loss. For scar quantification, the method was compared with the results or algorithms reported in the literature and demonstrated better performance.

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References

  1. Chen, C., Bai, W., Rueckert, D.: Multi-task learning for left atrial segmentation on GE-MRI. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 292–301. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-12029-0_32

    Chapter  Google Scholar 

  2. Chugh, S.S., et al.: Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study. Circulation 129(8), 837–847 (2014)

    Article  Google Scholar 

  3. Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

    Article  Google Scholar 

  4. Karim, R., et al.: A method to standardize quantification of left atrial scar from delayed-enhancement MR images. IEEE J. Transl. Eng. Health Med. 2, 1–15 (2014)

    Article  Google Scholar 

  5. Karim, R., et al.: Algorithms for left atrial wall segmentation and thickness-evaluation on an open-source CT and MRI image database. Med. Image Anal. 50, 36–53 (2018)

    Article  Google Scholar 

  6. Karim, R., et al.: Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge. J. Cardiovasc. Magn. Reson. 15(1), 105 (2013)

    Article  MathSciNet  Google Scholar 

  7. Li, L., et al.: Atrial scar quantification via multi-scale CNN in the graph-cuts framework. Med. Image Anal. 60, 101595 (2020)

    Article  Google Scholar 

  8. Liu, J., et al.: Myocardium segmentation from DE MRI using multicomponent Gaussian mixture model and coupled level set. IEEE Trans. Biomed. Eng. 64(11), 2650–2661 (2017)

    Article  Google Scholar 

  9. Nuñez-Garcia, M., et al.: Left atrial segmentation combining multi-atlas whole heart labeling and shape-based atlas selection. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 302–310. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12029-0_33

    Chapter  Google Scholar 

  10. Ravanelli, D., et al.: A novel skeleton based quantification and 3-D volumetric visualization of left atrium fibrosis using late gadolinium enhancement magnetic resonance imaging. IEEE Trans. Med. Imaging 33(2), 566–576 (2013)

    Article  Google Scholar 

  11. Xiong, Z., Fedorov, V.V., Fu, X., Cheng, E., Macleod, R., Zhao, J.: Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. IEEE Trans. Med. Imaging 38(2), 515–524 (2018)

    Article  Google Scholar 

  12. Yu, L., Wang, S., Li, X., Fu, C.W., Heng, P.A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 605–613. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_67

    Chapter  Google Scholar 

  13. Zeng, Q., et al.: Liver segmentation in magnetic resonance imaging via mean shape fitting with fully convolutional neural networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_28

    Chapter  Google Scholar 

  14. Zhao, J., Xiong, Z.: 2018 atrial segmentation challenge (2018). http://atriaseg2018.cardiacatlas.org/

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (61971142), and L. Li was partially supported by the CSC Scholarship.

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Correspondence to Xiahai Zhuang .

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Li, L., Weng, X., Schnabel, J.A., Zhuang, X. (2020). Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12264. Springer, Cham. https://doi.org/10.1007/978-3-030-59719-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-59719-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59718-4

  • Online ISBN: 978-3-030-59719-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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