Abstract
Accurate segmentation of right ventricle (RV) from cardiac MRI is essential to evaluate the structure and function of the RV and to further study cardiac disorders. However, it is a difficult task due to its complex crescent shape and the presence of wall irregularities in its cavity. As part of the multi-disease, multi-center, and multi-view RV segmentation in cardiac MRI challenge (M&Ms-2), we propose to solve the problem using a fully automatic deep learning method that employs different data augmentation techniques. More specifically, we applied MRI-specific based, intensity and spatial data augmentation techniques to reduce the variation among the multi-center images with various cardiac pathologies. MRI-specific data augmentation are transformations that simulate image artifacts specific to MRI such as random bias field, random ghosting and random motion artifacts. We evaluate the proposed method in the validation set of the challenge. Among the data augmentation techniques applied, the MRI-specific based data augmentation enhanced the segmentation results of both long-axis and short-axis images in terms of Dice coefficient and Hausdorff Distance (HD). From the experiments, it shows us that the usage of MRI-specific transformations alongside intensity and spatial transformations in cardiac MRI can increase the variety of the training dataset and further help to improve the generalization capabilities of the models in multi-center, multi-disease cardiac MRI images. The proposed method ranked second at the M&Ms-2 challenge.
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References
Bai, W., et al.: Self-supervised learning for cardiac MR image segmentation by anatomical position prediction. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 541–549. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_60
Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37, 2514–2525 (2018)
Campello, V.M., et. al: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge. IEEE Trans. Med. Imaging 40, 3543–3554 (2021)
Caudron, J., Fares, J., Vivier, P., Lefebvre, V., Petitjean, C., Dacher, J.: Diagnostic accuracy and variability of three semi-quantitative methods for assessing right ventricular systolic function from cardiac MRI in patients with acquired heart disease. Eur. Radiol. 21, 2111–2120 (2011)
Full, P.M., Isensee, F., Jäger, P.F., Maier-Hein, K.: Studying robustness of semantic segmentation under domain shift in cardiac MRI. In: Puyol Anton, E., et al. (eds.) STACOM 2020. LNCS, vol. 12592, pp. 238–249. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68107-4_24
Girum, K.B., Créhange, G., Lalande, A.: Learning with context feedback loop for robust medical image segmentation. IEEE Trans. Med. Imaging 40, 1542–1554 (2021)
Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15, 850–863 (1993)
Isensee, F., Jaeger, P., Kohl, S., Petersen, J., Maier-Hein, K.: NNU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 1–9 (2021). https://doi.org/10.1038/s41592-020-01008-z
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
Juntu, J., Sijbers, J., Dyck, D., Gielen, J.: Bias Field Correction for MRI Images. In: Kurzyski, M., Puchała, E., Woźniak, M., Żołnierek, A. (eds.) Computer Recognition Systems. Advances in Soft Computing, vol. 30. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-32390-2_64
Luo, G., An, R., Wang, K., Dong, S., Zhang, H.: A deep learning network for right ventricle segmentation in short-axis MRI. In: 2016 Computing in Cardiology Conference (CinC), pp. 485–488 (2016)
Ma, J.: Histogram matching augmentation for domain adaptation with application to multi-centre, multi-vendor and multi-disease cardiac image segmentation. In: Puyol Anton, E. (ed.) STACOM 2020. LNCS, vol. 12592, pp. 177–186. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68107-4_18
Painchaud, N., Skandarani, Y., Judge, T., Bernard, O., Lalande, A., Jodoin, P.-M.: Cardiac MRI segmentation with strong anatomical guarantees. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 632–640. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_70
Pérez-García, F., Sparks, R., Ourselin, S.: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. ArXiv abs/2003.04696 (2020)
Petitjean, C., et al.: Right ventricle segmentation from cardiac MRI: a collation study. Med. Image Anal. 19(1), 187–202 (2015)
Vigneault, D., Xie, W., Ho, C., Bluemke, D., Noble, J.: \({\Omega }\)-Net (Omega-Net): fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks. Med. Image Anal. 48, 95–106 (2018)
Zhang, Y., et al.: Semi-supervised cardiac image segmentation via label propagation and style transfer. In: Puyol Anton, E., et al. (eds.) STACOM 2020. LNCS, vol. 12592, pp. 219–227. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68107-4_22
Acknowledgements
This work was supported by the French National Research Agency (ANR), with reference ANR-19-CE45-0001-01-ACCECIT. Calculations were performed using HPC resources from DNUM CCUB (Centre de Calcul de l’Université de Bourgogne). We also thank the Mesocentre of Franche-Comté for the computing facilities.
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Arega, T.W., Legrand, F., Bricq, S., Meriaudeau, F. (2022). Using MRI-specific Data Augmentation to Enhance the Segmentation of Right Ventricle in Multi-disease, Multi-center and Multi-view Cardiac MRI. In: Puyol Antón, E., et al. Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science(), vol 13131. Springer, Cham. https://doi.org/10.1007/978-3-030-93722-5_27
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DOI: https://doi.org/10.1007/978-3-030-93722-5_27
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