Abstract
Segmentation of anatomical structures from Cardiac Magnetic Resonance (CMR) is central to the non-invasive quantitative assessment of cardiac function and structure, and deep-learning-based automatic segmentation models prove to have satisfying performance. However, patients’ respiratory motion during the scanning process can greatly degenerate the quality of CMR images, resulting in a serious performance drop for deep learning algorithms. Building a robust cardiac MRI segmentation model is one of the keys to facilitating the use of deep learning in practical clinic scenarios. To this end, we experiment with several network architectures and compare their segmentation accuracy and robustness to respiratory motion. We further pre-train our network on large publicly available CMR datasets and augment our training set with adversarial augmentation, both methods bring significant improvement. We evaluate our methods on the cine MRI dataset of the CMRxMotion challenge and obtain promising performance for the segmentation of the left ventricle, left ventricular myocardium, and right ventricle.
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References
Schulz-Menger, J., et al.: Standardized image interpretation and post-processing in cardiovascular magnetic resonance - 2020 update: Society for Cardiovascular Magnetic Resonance (SCMR): Board of Trustees Task Force on Standardized Post-Processing. J. Cardiovasc. Magn. Reson. 22(1), 19 (2022). https://doi.org/10.1186/s12968-020-00610-6
Alfakih, K., Plein, S., Thiele, H., Jones, T., Ridgway, J.P., Sivananthan, M.U.: Normal human left and right ventricular dimensions for MRI as assessed by turbo gradient echo and steady-state free precession imaging sequences. J. Magn. Reson. Imaging 17(3), 323–329 (2003). https://doi.org/10.1002/jmri.10262
Bai, W., et al.: A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion. Med. Image Anal. 26(1), 133–45 (2015). https://doi.org/10.1016/j.media.2015.08.009
Bai, W., et al.: Biventricular surface reconstruction from cine MRI contours using point completion networks. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 105–109 (2021). https://doi.org/10.1109/ISBI48211.2021.9434040
Chen, C., et al.: Enhancing MR image segmentation with realistic adversarial data augmentation. arXiv preprint (2022). https://arxiv.org/abs/2108.03429
Corral Acero, J., et al.: SMOD - data augmentation based on statistical models of deformation to enhance segmentation in 2D cine cardiac MRI. In: Coudière, Y., Ozenne, V., Vigmond, E., Zemzemi, N. (eds.) FIMH 2019. LNCS, vol. 11504, pp. 361–369. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21949-9_39
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: International Conference on Learning Representations, pp. 1–23 (2017). https://arxiv.org/abs/1706.06083
Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation (2021). https://arxiv.org/abs/2105.05537
Chen, C., et al.: Realistic adversarial data augmentation for MR image segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020, Part I. LNCS, vol. 12261, pp. 667–677. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_65
Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable ConvNets V2: more deformable, better results, In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9300–9308 (2019). https://doi.org/10.1109/CVPR.2019.00953
Liu, Z., et al.: Swin Transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3464–3473 (2021). https://doi.org/10.48550/arXiv.2103.14030
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
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. LNCS, vol. 12962. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08999-2_22
Isensee, F., Jaeger, P.F., Kohl, S.A.A., et al.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203–211 (2021). https://doi.org/10.1038/s41592-020-01008-z
Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the Problem Solved? IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018). https://doi.org/10.1109/TMI.2018.2837502
Campello, V.M., et al.: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M &Ms Challenge. IEEE Trans. Med. Imaging 40(12), 3543–3554 (2021). https://doi.org/10.1109/TMI.2021.3090082
Zhuang, X.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 2933–2946 (2019). https://doi.org/10.1109/TPAMI.2018.2869576
Zhuang, X.: Multivariate mixture model for cardiac segmentation from multi-sequence MRI. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 581–588 (2016). https://doi.org/10.1007/978-3-319-46723-8_67
Deng J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009). https://doi.org/10.1109/CVPR.2009.5206848
Fulton, M.J., Heckman, C.R., Rentschler, M.E.: Deformable Bayesian convolutional networks for disease-robust cardiac MRI segmentation. In: Puyol Antón, E., et al. (eds.) STACOM 2021. LNCS, vol. 13131, pp. 296–305. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-93722-5_32
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014). https://doi.org/10.48550/arXiv.1411.1792
Zhuang, F., et al.: A comprehensive survey on transfer learning. arXiv preprint (2019). https://arxiv.org/abs/1911.02685
He, K., Girshick, R., Dollar, P.: Rethinking imagenet pre-training. arXiv preprint (2018). https://arxiv.org/abs/1811.08883
Raghu, M., Zhang, C., Kleinberg, J., Bengio, S.: Transfusion: understanding transfer learning for medical imaging. In: Advances in Neural Information Processing Systems, pp. 3347–3357 (2019). https://doi.org/10.48550/arXiv.1902.07208
Hendrycks, D., Lee, K., Mazeika, M.: Using pre-training can improve model robustness and uncertainty. In: Proceedings of the International Conference on Machine Learning (2019). https://doi.org/10.48550/arXiv.1901.09960
Mathis, A., et al.: Pretraining boosts out-of-domain robustness for pose estimation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1859–1868 (2019). https://doi.org/10.48550/arXiv.1909.11229
Tang, Y., et al.: Self-supervised pre-training of swin transformers for 3D medical image analysis. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20730–20740 (2022). https://doi.org/10.48550/arXiv.2111.14791
Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A.J., Wright, G.A.: Evaluation framework for algorithms segmenting short axis cardiac MRI. MIDAS J. Cardiac MR Left Ventricle Segmentation Challenge (2009). https://hdl.handle.net/10380/3070
Petitjean, C., Zuluaga, M.A., Bai, W., et al.: Right ventricle segmentation from cardiac MRI: a collation study. Med. Image Anal. 19(1), 187–202 (2015). https://doi.org/10.1016/j.media.2014.10.004
Tobon-Gomez, C., Geers, A.J., Peters, J., et al.: Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets. IEEE Trans. Med. Imaging 34(7), 1460–1473 (2015). https://doi.org/10.1109/TMI.2015.2398818
Second annual data science bowl: transforming how we diagnose heart disease (2015). https://www.kaggle.com/competitions/second-annual-data-science-bowl/overview
Wang, S., Qin, C., Wang, C., Wang, K., Wang, H., Chen, C., et al.: The extreme cardiac MRI analysis challenge under respiratory motion (CMRxMotion). arXiv preprint (2022). https://doi.org/10.48550/arXiv.2210.06385
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Gong, S., Lu, W., Xie, J., Zhang, X., Zhang, S., Dou, Q. (2022). Robust Cardiac MRI Segmentation with Data-Centric Models to Improve Performance via Intensive Pre-training and Augmentation. In: Camara, O., et al. Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers. STACOM 2022. Lecture Notes in Computer Science, vol 13593. Springer, Cham. https://doi.org/10.1007/978-3-031-23443-9_47
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