Abstract
Recently, developments have been made towards modelling patient-specific deformable mitral valves from transesophageal echocardiography (TEE). Thus far, a major limitation in the workflow has been the manual process of segmentation and model profile definition. Completing a manual segmentation from 3D TEE can take upwards of two hours, and existing automated segmentation approaches have limitations in both computation time and accuracy. Streamlining the process of segmenting the valve and generating a surface mold is important for the scalability and accuracy of patient-specific mitral valve modelling. We present DeepMitral, a fully automatic, deep learning based mitral valve segmentation approach that can quickly and accurately extract the geometry of the mitral valve directly from TEE volumes. We developed and tested our model on a data set comprising 48 diagnostic TEE volumes with corresponding segmentations from mitral valve intervention patients. Our proposed pipeline is based on the Residual UNet architecture with five layers. Evaluation of our proposed pipeline was assessed using manual segmentations performed by two clinicians as a gold-standard. The comparisons are made using the mean absolute surface distance (MASD) between the boundaries of the complete segmentations, as well as the 95% Hausdorff distances. DeepMitral achieves a MASD of \({0.59 \pm 0.23}\mathrm{mm}\) and average 95% Hausdorff distance of \({1.99 \pm 1.14}\mathrm{mm}\). Additionally, we report a Dice score of 0.81. The resulting segmentations from our approach successfully replicate gold-standard segmentations with improved performance over existing state-of-the-art methods. DeepMitral improves the workflow of the mitral valve modelling process by reducing the time required for completing an accurate mitral valve segmentation, and providing more consistent results by removing user variability from the segmentation process.
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References
Ailawadi, G., et al.: Is mitral valve repair superior to replacement in elderly patients? Ann. Thorac. Surg. 86(1), 77–86 (2008)
Andreassen, B.S., Veronesi, F., Gerard, O., Solberg, A.H.S., Samset, E.: Mitral annulus segmentation using deep learning in 3-D transesophageal echocardiography. IEEE J. Biomed. Health Inf. 24(4), 994–1003 (2020)
Benjamin, E.J., et al.: Heart disease and stroke statistics—2018 update: a report from the American heart association. Circulation 137(12), E67–E492 (2018)
Burlina, P., et al.: Patient-specific modeling and analysis of the mitral valve using 3D-TEE. In: Navab, N., Jannin, P. (eds.) IPCAI 2010. LNCS, vol. 6135, pp. 135–146. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13711-2_13
Carnahan, P., et al.: Interactive-automatic segmentation and modelling of the mitral valve. In: Coudière, Y., Ozenne, V., Vigmond, E., Zemzemi, N. (eds.) FIMH 2019. LNCS, vol. 11504, pp. 397–404. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21949-9_43
Costa, E., et al.: Mitral valve leaflets segmentation in echocardiography using convolutional neural networks. In: 2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG), IEEE (February 2019)
Eleid, M.F., et al.: The learning curve for transcatheter mitral valve repair with MitraClip. J. Interv. Cardiol. 29(5), 539–545 (2016)
Ginty, O.K., et al.: Dynamic, patient-specific mitral valve modelling for planning transcatheter repairs. Int. J. Comput. Assist. Radiol. Surg. 14(7), 1227–1235 (2019)
Holzhey, D.M., Seeburger, J., Misfeld, M., Borger, M.A., Mohr, F.W.: Learning minimally invasive mitral valve surgery. Circulation 128(5), 483–491 (2013)
Ionasec, R.I., et al.: Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE. IEEE Trans. Med. Imaging 29(9), 1636–1651 (2010)
Jassar, A.S., et al.: Quantitative mitral valve modeling using real-time three-dimensional echocardiography: technique and repeatability. Ann. Thorac. Surg. 91(1), 165–171 (2011)
Kerfoot, E., Clough, J., Oksuz, I., Lee, J., King, A.P., Schnabel, J.A.: Left-Ventricle Quantification Using Residual U-Net. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 371–380. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12029-0_40
Kingma, D.P., Ba, J.L.: Adam: A Method for Stochastic Optimization. In: CoRR, vol. 1412.6980 (2014)
Kozlowski, P., Bandaru, R.S., D’hooge, J., Samset, E.: Real-time catheter localization and visualization using three-dimensional echocardiography. In: Webster, R.J., Fei, B. (eds.) Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling. SPIE (Mar 2017)
Liu, S., et al.: 3D anisotropic hybrid network: transferring convolutional features from 2D images to 3D anisotropic volumes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 851–858. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_94
Mashari, A., et al.: Hemodynamic testing of patient-specific mitral valves using a pulse duplicator: a clinical application of three-dimensional printing. J. Cardiothorac. Vasc. Anesth. 30(5), 1278–1285 (2016)
McNeely, C.A., Vassileva, C.M.: Long-term outcomes of mitral valve repair versus replacement for degenerative disease: a systematic review. Curr. Cardiol. Rev. 11(2), 157–62 (2015). http://www.ncbi.nlm.nih.gov/pubmed/25158683
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional Neural Networks for Volumetric Medical Image Segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (October 2016)
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28
Pouch, A.M., et al.: Fully automatic segmentation of the mitral leaflets in 3D transesophageal echocardiographic images using multi-atlas joint label fusion and deformable medial modeling. Med. Image Anal. 18(1), 118–129 (2014)
Ray, S.: Changing epidemiology and natural history of valvular heart disease. Clin. Med. 10(2), 168–171 (2010)
Scanlan, A.B., et al.: Comparison of 3D echocardiogram-derived 3D printed valve models to molded models for simulated repair of pediatric atrioventricular valves. Pediatr. Cardiol. 39(3), 538–547 (2017)
Schneider, R.J., Tenenholtz, N.A., Perrin, D.P., Marx, G.R., del Nido, P.J., Howe, R.D.: Patient-specific mitral leaflet segmentation from 4D ultrasound. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 520–527. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23626-6_64
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We would like to acknowledge the following sources of funding: Canadian Institutes for Health Research, Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Innovation.
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Carnahan, P., Moore, J., Bainbridge, D., Eskandari, M., Chen, E.C.S., Peters, T.M. (2021). DeepMitral: Fully Automatic 3D Echocardiography Segmentation for Patient Specific Mitral Valve Modelling. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_44
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