Abstract
Atrial fibrillation (AF) is the most common sustained heart rhythm disturbance and a leading cause of hospitalization, heart failure and stroke. In the current medical practice, atrial segmentation from medical images for clinical diagnosis and treatment, is a labor-intensive and error-prone manual process. The atrial segmentation challenge held in conjunction with the 2018 the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) conference and Statistical Atlases and Computational Modelling of the Heart (STACOM), offered the opportunity to develop reliable approaches to automatically annotate and perform segmentation of the left atrial (LA) chamber using the largest available 3D late gadolinium-enhanced MRI (LGE-MRI) dataset with 154 3D LGE-MRIs and labels. For this challenge, 11 out the 27 contestants achieved more than 90% Dice score accuracy, however, a critical question remains as which is the optimal approach for LA segmentation. In this paper, we propose a two-stage 2D fully convolutional neural network with extensive data augmentation and achieves a superior segmentation accuracy with a Dice score of 93.7% using the same dataset and conditions as for the atrial segmentation challenge. Thus, our approach outperforms the methods proposed in the atrial segmentation challenge while employing less computational resources than the challenge winning method.
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Krijthe, B.P., Kunst, A., Benjamin, E.J., Lip, G.Y., Franco, O.H., Hofman, A., et al.: Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. Eur. Heart J. 34(35), 2746–2751 (2013)
Brooks, A.G., Stiles, M.K., Laborderie, J., Lau, D.H., Kuklik, P., Shipp, N.J., et al.: Outcomes of long-standing persistent atrial fibrillation ablation: a systematic review. Heart Rhythm 7(6), 835–846 (2010)
Oakes, R.S., Badger, T.J., Kholmovski, E.G., Akoum, N., Burgon, N.S., et al.: Detection and quantification of left atrial structural remodeling with delayed-enhancement magnetic resonance imaging in patients with atrial fibrillation. Circulation 119(13), 1758–1767 (2009)
Mortazi, A., Karim, R., Kawal, R., Burt, J., Bagci, U.: CardiacNET: segmentation of left atrium and proximal pulmonary veins from MRI using multi-view CNN. arXiv:170506333 (2017)
Tobon-Gomez, C., Geers, A.J., Peters, J., Weese, J., Pinto, K., Karim, R., et al.: Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets. IEEE Trans. Med. Imaging 34(7), 1460–1473 (2015)
Xiong, Z., Zhao, J., Stiles, M.: Machine learning for fully automatic 3D atria segmentation and reconstruction from gadolinium enhanced MRIs. Heart Lung Circ. 26, S33 (2017)
Pop, M., et al. (eds.): Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges: 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-12029-0
Xia, Q., Yao, Y., Hu, Z., Hao, A.: Automatic 3D atrial segmentation from GE-MRIs using volumetric fully convolutional networks. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 211–220. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12029-0_23
Buda, M., Maki, A., Mazurowski, M.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249–259 (2018)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE (2016)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Orhan, E., Pitkow, X.: Skip connections eliminate singularities. arXiv:1701.09175 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
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Jamart, K., Xiong, Z., Talou, G.M., Stiles, M.K., Zhao, J. (2020). Two-Stage 2D CNN for Automatic Atrial Segmentation from LGE-MRIs. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM 2019. Lecture Notes in Computer Science(), vol 12009. Springer, Cham. https://doi.org/10.1007/978-3-030-39074-7_9
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DOI: https://doi.org/10.1007/978-3-030-39074-7_9
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