Abstract
We present a robust and accurate method for multi-atlas segmentation of whole heart MRI scans. After preprocessing, which includes resampling to isotropic voxel sizes and cropping or padding to same dimensions, all training scans are registered linearly and nonlinearly to an unseen set of test scans. We employ the efficient discrete registration framework called deeds that captures large shape variations across scans, performed best in a recent registration comparison on abdominal scans and requires less than 2 min of computation time per scan. Subsequently, we perform multi-atlas label fusion using a non-local means approach with a normalised SSD metric and a fast implementation using boxfilters. Subsequently, a multi-label random walk is performed on the obtained probability maps for an edge-preserving smoothing. Without performing any domain-specific parameter tuning, we obtained a Dice accuracy of 86.0% (averaged across 7 labels) and 87.0% for the whole heart on the MRI test dataset, which is the first rank of the MICCAI 2017 challenge. The segmentations are also visually very smooth using this fully automatic method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Grothues, F., Smith, G.C., Moon, J.C., Bellenger, N.G., Collins, P., Klein, H.U., Pennell, D.J.: Comparison of interstudy reproducibility of cardiovascular magnetic resonance with 2D echocardiography in normal subjects and in patients with heart failure or left ventricular hypertrophy. Am. J. Cardiol. 90(1), 29–34 (2002)
Ramanathan, C., Ghanem, R.N., Jia, P., Ryu, K., Rudy, Y.: Noninvasive electrocardiographic imaging for cardiac electrophysiology and arrhythmia. Nat. Med. 10(4), 422–428 (2004)
Vuissoz, P.A., Odille, F., Fernandez, B., Lohezic, M., Benhadid, A., Mandry, D., Felblinger, J.: Free-breathing imaging of the heart using 2D cine-GRICS with assessment of ventricular volumes and function. J. Magn. Reson Imaging 35(2), 340–351 (2012)
Nazarian, S., Bluemke, D.A., Lardo, A.C., Zviman, M.M., Watkins, S.P., Dickfeld, T.L., Meininger, G.R., Roguin, A., Calkins, H., Tomaselli, G.F., et al.: Magnetic resonance assessment of the substrate for inducible ventricular tachycardia in nonischemic cardiomyopathy. Circulation 112(18), 2821–2825 (2005)
Nielles-Vallespin, S., Mekkaoui, C., Gatehouse, P., Reese, T.G., Keegan, J., Ferreira, P.F., Collins, S., Speier, P., Feiweier, T., Silva, R., et al.: In vivo diffusion tensor MRI of the human heart: reproducibility of breath-hold and navigator-based approaches. Magn. Reson. Med. 70(2), 454–465 (2013)
Tobon-Gomez, C., Geers, A.J., Peters, J., Weese, J., Pinto, K., Karim, R., Ammar, M., Daoudi, A., Margeta, J., Sandoval, Z., et al.: Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets. IEEE Trans. Med. Imag. 34(7), 1460–1473 (2015)
Kutra, D., Saalbach, A., Lehmann, H., Groth, A., Dries, S.P.M., Krueger, M.W., Dössel, O., Weese, J.: Automatic multi-model-based segmentation of the left atrium in cardiac MRI scans. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 1–8. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33418-4_1
Zhuang, X., Rhode, K.S., Razavi, R.S., Hawkes, D.J., Ourselin, S.: A registration-based propagation framework for automatic whole heart segmentation of cardiac MRI. IEEE Trans. Med. Imag. 29(9), 1612–1625 (2010)
Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med. Imag. Anal. 31, 77–87 (2016)
Wolterink, J.M., Leiner, T., Viergever, M.A., Išgum, I.: Dilated convolutional neural networks for cardiovascular MR segmentation in congenital heart disease. In: Zuluaga, M.A., Bhatia, K., Kainz, B., Moghari, M.H., Pace, D.F. (eds.) RAMBO/HVSMR -2016. LNCS, vol. 10129, pp. 95–102. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52280-7_9
Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Guerrero, R., Cook, S., de Marvao, A., O’Regan, D., et al.: Anatomically constrained neural networks (ACNN): Application to cardiac image enhancement and segmentation. arXiv preprint arXiv:1705.08302 (2017)
Heinrich, M., Jenkinson, M., Brady, J., Schnabel, J.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE Trans. Med. Imag. 32(7), 1239–1248 (2013)
Xu, Z., Lee, C., Heinrich, M., Modat, M., Rueckert, D., Ourselin, S., Abramson, R., Landman, B.: Evaluation of six registration methods for the human abdomen on clinically acquired CT. IEEE Trans. Biomed. Eng. 1–10 (2016)
Heinrich, M.P., Jenkinson, M., Papież, B.W., Brady, S.M., Schnabel, J.A.: Towards realtime multimodal fusion for image-guided interventions using self-similarities. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 187–194. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40811-3_24
Wang, H., Suh, J.W., Das, S.R., Pluta, J.B., Craige, C., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion. IEEE Trans. Patt. Anal. Mach. Intell. 35(3), 611–623 (2013)
Asman, A.J., Landman, B.A.: Non-local statistical label fusion for multi-atlas segmentation. Med. Imag. Anal. 17(2), 194–208 (2013)
Heinrich, M.P., Simpson, I., Papież, B., Brady, J., Schnabel, J.: Deformable image registration by combining uncertainty estimates from supervoxel belief propagation. Med. Imag. Anal. 27, 57–71 (2016)
Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)
Heinrich, M.P., Papież, B.W., Schnabel, J.A., Handels, H.: Non-parametric discrete registration with convex optimisation. In: Ourselin, S., Modat, M. (eds.) WBIR 2014. LNCS, vol. 8545, pp. 51–61. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08554-8_6
Langerak, T., Van Der Heide, U., Kotte, A., Viergever, M., Van Vulpen, M., Pluim, J.: Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Trans. Med. Imag. 29(12), 2000–2008 (2010)
Xu, Z., Asman, A.J., Shanahan, P.L., Abramson, R.G., Landman, B.A.: SIMPLE is a good idea (and better with context learning). In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 364–371. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10404-1_46
Grady, L.: Multilabel random walker image segmentation using prior models. In: CVPR, pp. 763–770 (2005)
Heinrich, M.P., Blendowski, M.: Multi-organ segmentation using vantage point forests and binary context features. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 598–606. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_69
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Proceedings of NIPS, pp. 2–9 (2011)
Oguz, I., Kashyap, S., Wang, H., Yushkevich, P., Sonka, M.: Globally optimal label fusion with shape priors. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 538–546. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_62
Bai, W., Shi, W., Ledig, C., Rueckert, D.: Multi-atlas segmentation with augmented features for cardiac MR images. Med. Imag. Anal. 19(1), 98–109 (2015)
Acknowledgements
We would like to thank the organisers of the MM-WHS 2017 for providing this rich new dataset to the public, which enables the evaluation of new algorithms for the problem of detailed 3D heart segmentation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Heinrich, M.P., Oster, J. (2018). MRI Whole Heart Segmentation Using Discrete Nonlinear Registration and Fast Non-local Fusion. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science(), vol 10663. Springer, Cham. https://doi.org/10.1007/978-3-319-75541-0_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-75541-0_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75540-3
Online ISBN: 978-3-319-75541-0
eBook Packages: Computer ScienceComputer Science (R0)