Abstract
Gadolinium contrast agents are used in a third of all magnetic resonance scans to study the extent of fibrosis across the left atria in patients with atrial fibrillation. Direct segmentation of the atrial heart chambers from these 3D gadolinium-enhanced magnetic resonance images (GE-MRI) is very demanding due to the low contrast between the atrial tissue and background. Current automatic segmentation methods delineate the left atrium on auxiliary bright-blood MRI scans, which need to be registered to GE-MRI in an additional, potentially error-prone step. Yet, it could render extremely useful to obtain direct segmentation on GE-MRI to simultaneously estimate the left atrium anatomy and the extent of fibrosis. In this work, we present a deep learning approach which is able to segment the left atrium from 3D GE-MRI. 100 data sets provided by the MICCAI 2018 Atrial Segmentation Challenge have been used to train and test deep convolutional neural networks (CNN), which follow a 2D architecture with deep supervision (train-validation-test 70-5-25). After performing a four fold cross validation, the network achieved a mean dice of 0.8945. Within the scope of the test phase of the challenge, we trained the network on 100 data sets, predicted novel segmentations on the official test set and, according to the leaderboard, achieved a final score of 0.888.
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References
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. Imag. 33(2), 566–576 (2014)
Karim, R., Mohiaddin, R., Rueckert, D.: Left atrium segmentation for atrial fibrillation ablation. In: Proceedings of the SPIE 6918, Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling, 69182U, 15 March 2008. https://doi.org/10.1117/12.771023
Lombaert, H., Sun, Y., Grady, L., Xu, C.: A multilevel banded graph cuts method for fast image segmentation. In: 10th IEEE International Conference Computer Vision, vol. 1, pp. 259–265 (2005)
Peters, J., Ecabert, O., Meyer, C., Schramm, H., Kneser, R., Groth, A., Weese, J.: Automatic whole heart segmentation in static magnetic resonance image volumes. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4792, pp. 402–410. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75759-7_49
Kim, R.J., et al.: The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. N. Engl. J. Med. 343, 1445–1453 (2000). https://doi.org/10.1056/NEJM200011163432003
Zhu, L., Gao, Y., Yezzi, A., Tannenbaum, A.: Automatic segmentation of the left atrium from MR images via variational region growing with a moments-based shape prior. IEEE Trans. Image Process. 22(12), 5111–5122 (2013)
Yang, G., et al.: A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced MRI images. In: 2017 IEEE 14th International Symposium on Biomedical Imaging
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
Tobon-Gomez, C., et al.: Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets. IEEE Trans. Med. Imag. 34(7), 1460–1473 (2015)
Marrouche, N.F., et al.: Association of atrial tissue fibrosis identified by delayed enhancement MRI and atrial fibrillation catheter ablation: the DECAAF study. JAMA 311(5), 498–506 (2014). https://doi.org/10.1001/jama.2014.3
Litjens, G., et al.: A survey on deep learning in medical image analysis Medical Image Analysis. Med. Image Anal. 42, 60–88 (2007)
Mortazi, A., Karim, R., Rhode, K., Burt, J., Bagci, U.: CardiacNET: segmentation of left atrium and proximal pulmonary veins from MRI using multi-view CNN. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 377–385. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_43
Isensee, F., Jaeger, P.F., Full, P.M., Wolf, I., Engelhardt, S., Maier-Hein, K.H.: Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 120–129. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0_13
Kayalibay, B., Jensen, G., van der Smagt, P.: CNN-based segmentation of medical imaging data, arXiv preprint arXiv:1701.03056 (2017)
McGann, C., Akoum, N., Patel, A., et al.: Atrial fibrillation ablation outcome is predicted by left atrial remodeling on MRI. Circ. Arrhythmia Electrophysiol. 7(1), 23–30 (2014). https://doi.org/10.1161/CIRCEP.113.000689
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Acknowledgements
The work was supported by the European Regional Development Fund under the operation number ‘ZS/2016/04/78123’ as part of the initiative “Sachsen-Anhalt WISSENSCHAFT Schwerpunkte” and by the German Research Foundation (DFG) project 398787259, EN1197/2-1.
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Preetha, C.J., Haridasan, S., Abdi, V., Engelhardt, S. (2019). Segmentation of the Left Atrium from 3D Gadolinium-Enhanced MR Images with Convolutional Neural Networks. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science(), vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_29
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