Abstract
Accurate biventricular segmentation of cardiac magnetic resonance (CMR) cine images is essential for the clinical evaluation of heart function. However, compared to left ventricle (LV), right ventricle (RV) segmentation is still more challenging and less reproducible. Degenerate performance frequently occurs at the RV base, where the in-plane anatomical structures are complex (with atria, valve, and aorta) and vary due to the strong interplanar motion. In this work, we propose to address the currently unsolved issues in CMR segmentation, specifically at the RV base, with two strategies: first, we complemented the public resource by reannotating the RV base in the ACDC dataset, with refined delineation of the right ventricle outflow tract (RVOT), under the guidance of an expert cardiologist. Second, we proposed a novel dual encoder U-Net architecture that leverages temporal incoherence to inform the segmentation when interplanar motions occur. The inter-planar motion is characterized by loss-of-tracking, via Bayesian uncertainty of a motion-tracking model. Our experiments showed that our method significantly improved RV base segmentation taking into account temporal incoherence. Furthermore, we investigated the reproducibility of deep learning-based segmentation and showed that the combination of consistent annotation and loss of tracking could enhance the reproducibility of RV segmentation, potentially facilitating a large number of clinical studies focusing on RV.
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References
Arega, T.W., Legrand, F., Bricq, S., Meriaudeau, F.: 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. (eds.) STACOM 2021. LNCS, vol. 13131, pp. 250–258. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-93722-5_27
Bai, W.: Recurrent neural networks for aortic image sequence segmentation with sparse annotations. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018, Part IV. LNCS, vol. 11073, pp. 586–594. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_67
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)
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(11), 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(12), 3543–3554 (2021)
Chen, T., Fox, E., Guestrin, C.: Stochastic gradient Hamiltonian Monte Carlo. In: International Conference on Machine Learning, pp. 1683–1691. PMLR (2014)
Dong, S., et al.: DeU-net: deformable U-net for 3D cardiac MRI video segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020, Part IV. LNCS, vol. 12264, pp. 98–107. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_10
Farré, J., Anderson, R.H., Cabrera, J.A., Sánchez-Quintana, D., Rubio, J.M., Benezet-Mazuecos, J.: Cardiac anatomy for catheter mapping and ablation of arrhythmias. Catheter Ablation Cardiac Arrhythmias 74–102 (2011)
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning [eb/ol]. arXiv preprint arxiv:1506.02142 (2015)
Han, Y., et al.: Ranolazine improves right ventricular function in patients with precapillary pulmonary hypertension: results from a double-blind, randomized, placebo-controlled trial. J. Cardiac Fail. 27(2), 253–257 (2021)
Ho, S., Nihoyannopoulos, P.: Anatomy, echocardiography, and normal right ventricular dimensions. Heart 92(suppl 1), i2–i13 (2006)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? Adv. Neural Inf. Process. Syst. 30 (2017)
Kikinis, R., Pieper, S.D., Vosburgh, K.G.: 3D slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz, F.A. (ed.) Intraoperative Imaging and Image-Guided Therapy, pp. 277–289. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-7657-3_19
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. arxiv e-prints, arXiv preprint arXiv:1612.01474, vol. 5 (2016)
Martín-Isla, C., et al.: Deep learning segmentation of the right ventricle in cardiac MRI: the M &Ms challenge. IEEE J. Biomed. Health Inform. (2023)
Nilsson, D., Sminchisescu, C.: Semantic video segmentation by gated recurrent flow propagation. arXiv preprint arXiv:1612.08871 (2016)
Qin, C., et al.: Joint learning of motion estimation and segmentation for cardiac MR image sequences. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018, Part II. LNCS, vol. 11071, pp. 472–480. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_53
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
Sheehan, F., Redington, A.: The right ventricle: anatomy, physiology and clinical imaging. Heart 94(11), 1510–1515 (2008)
Tao, Q., et al.: Deep learning-based method for fully automatic quantification of left ventricle function from cine MR images: a multivendor, multicenter study. Radiology 290(1), 81–88 (2019)
Wang, L., et al.: Diagnostic and prognostic value of right ventricular eccentricity index in pulmonary artery hypertension. Pulm. Circul. 10(2), 2045894019899778 (2020)
Wu, P., et al.: Cardiac MR image sequence segmentation with temporal motion encoding. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020, Part I. LNCS, vol. 12535, pp. 298–309. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66415-2_19
Yan, W., Wang, Y., van der Geest, R.J., Tao, Q.: Cine MRI analysis by deep learning of optical flow: adding the temporal dimension. Comput. Biol. Med. 111, 103356 (2019)
Yan, W., Wang, Y., Li, Z., van der Geest, R.J., Tao, Q.: Left ventricle segmentation via optical-flow-net from short-axis cine MRI: preserving the temporal coherence of cardiac motion. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018, Part IV. LNCS, vol. 11073, pp. 613–621. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_70
Yilmaz, P., Wallecan, K., Kristanto, W., Aben, J.P., Moelker, A.: Evaluation of a semi-automatic right ventricle segmentation method on short-axis MR images. J. Digit. Imaging 31, 670–679 (2018)
Zhao, Y., Simonetti, O., Han, Y., Tao, Q.: Artificial intelligence failure in cardiac magnetic resonance image segmentation: An empirical study. J. Cardiovasc. Magn. Reson. 26 (2024)
Zhao, Y., et al.: Bayesian uncertainty estimation by Hamiltonian Monte Carlo: applications to cardiac MRI segmentation (2024)
Zhao, Y., Yang, C., Schweidtmann, A., Tao, Q.: Efficient Bayesian uncertainty estimation for nnU-net. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13438, pp. 535–544. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16452-1_51
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The authors gratefully acknowledge the TU Delft AI Initiative for financial support.
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Zhao, Y., Zhang, Y., Simonetti, O., Han, Y., Tao, Q. (2024). Lost in Tracking: Uncertainty-Guided Cardiac Cine MRI Segmentation at Right Ventricle Base. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15009. Springer, Cham. https://doi.org/10.1007/978-3-031-72114-4_40
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