Abstract
Automatic segmentation of left atrial (LA) scars from late gadolinium enhanced CMR images is a crucial step for atrial fibrillation (AF) recurrence analysis. However, delineating LA scars is tedious and error-prone due to the variation of scar shapes. In this work, we propose a boundary-aware LA scar segmentation network, which is composed of two branches to segment LA and LA scars, respectively. We explore the inherent spatial relationship between LA and LA scars. By introducing a Sobel fusion module between the two segmentation branches, the spatial information of LA boundaries can be propagated from the LA branch to the scar branch. Thus, LA scar segmentation can be performed condition on the LA boundaries regions. In our experiments, 40 labeled images were used to train the proposed network, and the remaining 20 labeled images were used for evaluation. The network achieved an average Dice score of 0.608 for LA scar segmentation.
M. Wu and W. Ding—The two authors have equal contributions to the paper.
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References
Balk, E.M., Garlitski, A.C., Alsheikh-Ali, A.A., Terasawa, T., Chung, M., Ip, S.: Predictors of atrial fibrillation recurrence after radiofrequency catheter ablation: a systematic review. J. Cardiovasc. Electrophysiol. 21(11), 1208–1216 (2010)
Chen, J., et al.: Multiview two-task recursive attention model for left atrium and atrial scars segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 455–463. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_51
Heeringa, J., et al.: Prevalence, incidence and lifetime risk of atrial fibrillation: the Rotterdam study. Eur. Heart J. 27(8), 949–953 (2006)
Isensee, F., et al.: nnu-net: Self-adapting framework for U-net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)
Jefairi, N.A., et al.: Relationship between atrial scar on cardiac magnetic resonance and pulmonary vein reconnection after catheter ablation for paroxysmal atrial fibrillation. J. Cardiovasc. Electrophysiol. 30(5), 727–740 (2019)
Kanopoulos, N., Vasanthavada, N., Baker, R.L.: Design of an image edge detection filter using the Sobel operator. IEEE J. Solid-State Circuits 23(2), 358–367 (1988)
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), 1–17 (2013)
Kirchhof, P., Calkins, H.: Catheter ablation in patients with persistent atrial fibrillation. Eur. Heart J. 38(1), 20–26 (2017)
Li, L., Weng, X., Schnabel, J.A., Zhuang, X.: Joint left atrial segmentation and scar quantification based on a DNN with spatial encoding and shape attention. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 118–127. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_12
Li, L., Zimmer, V.A., Schnabel, J.A., Zhuang, X.: AtrialGeneral: domain generalization for left atrial segmentation of multi-center LGE MRIs. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 557–566. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_54
Li, L., Zimmer, V.A., Schnabel, J.A., Zhuang, X.: Atrialjsqnet: a new framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information. Med. Image Anal. 76, 102303 (2022)
Li, L., Zimmer, V.A., Schnabel, J.A., Zhuang, X.: Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: a review. Med. Image Anal. 102360 (2022)
Xu, Z., Wu, Z., Feng, J.: CFUN: combining faster R-CNN and U-net network for efficient whole heart segmentation. arXiv preprint arXiv:1812.04914 (2018)
Yang, G., et al.: Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention. Futur. Gener. Comput. Syst. 107, 215–228 (2020)
Zghaib, T., Nazarian, S.: New insights into the use of cardiac magnetic resonance imaging to guide decision making in atrial fibrillation management. Can. J. Cardiol. 34(11), 1461–1470 (2018)
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2019)
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Wu, M., Ding, W., Yang, M., Huang, L. (2023). Multi-depth Boundary-Aware Left Atrial Scar Segmentation Network. In: Zhuang, X., Li, L., Wang, S., Wu, F. (eds) Left Atrial and Scar Quantification and Segmentation. LAScarQS 2022. Lecture Notes in Computer Science, vol 13586. Springer, Cham. https://doi.org/10.1007/978-3-031-31778-1_2
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DOI: https://doi.org/10.1007/978-3-031-31778-1_2
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