Abstract
The anatomical delineation of heart cavities in CT scans is a well-known problem with extended applications in cardiopulmonary diseases. A common approach to provide multi-cavity segmentations is the use of atlas-based statistical shape models. This methodology is of particular interest when just non-contrast and non-gated scans are available since the non-observable internal structures of the heart can be inferred from the fitting of the pericardium provided by the shape model. These inferred cavities have shown predictive power in clinical studies when compared to reference standards. However, although promising, the shape model is limited to the fitting of the pericardium and cannot take advantage of the geometrical inter-relations of internal cavities. This leads to inaccurate segmentations of the smaller structures such as the atria and wrong estimations of the internal cavities. In this work, we study the potential of CNNs to learn the inter-relations of a multicavity active shape model to provide accurate estimations of the internal cavities in non-gated and non-contrast CT scans. We propose an architecture that is able to learn the appearance model of the heart from an inaccurate training dataset. Results demonstrate that our segmentation method improves the correlation of ventricular volume estimations when compared against a semiautomatic active shape approach using cardiac MRI as the reference standard.
This work has been partially funded by the National Institutes of Health NHLBI awards R01HL116473 and R01HL149877. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
Jemal, A., Fedewa, S.A.: Lung cancer screening with low-dose computed tomography in the United States–2010 to 2015 lung cancer screening with low-dose computed Tomography letters. JAMA Oncol. 3(9), 1278–1281 (2017)
Rahaghi, F.N., et al.: Ventricular geometry from non-contrast non-ECG-gated CT scans: an imaging marker of cardiopulmonary disease in smokers. Academic radiology 24(5), 594–602 (2017)
Bhatt, S.P., et al.: Cardiac morphometry on computed Tomography and exacerbation reduction with \(\beta \)-blocker therapy in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 196(11), 1484–1488 (2017)
Washko, G.R., et al.: Arterial vascular pruning, right ventricular size, and clinical outcomes in chronic obstructive pulmonary disease. A longitudinal observational study. Am. J. Respir. Crit. Care Med. 200(4), 454–461 (2019)
Washko, G.R., et al.: Smaller left ventricle size at noncontrast CT is associated with lower mortality in COPDGene participants. Radiology 296(1), 208–215 (2020)
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
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017)
Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation (2016)
Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1175–1183 (July 2017)
Roy, A.G., Navab, N., Wachinger, C.: Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 421–429. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_48
Hoogendoorn, C., et al.: A high-resolution atlas and statistical model of the human heart from multislice CT. IEEE Trans. Med. Imaging 32(1), 28–44 (2013)
Kamel, S.I., Levin, D.C., Parker, L., Rao, V.M.: Utilization trends in noncardiac thoracic imaging, 2002–2014. J. Am. Coll. Radiol. 14(3), 337–342 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Moreta-Martínez, R., Vegas Sánchez-Ferrero, G., Andresen, L., Qvortrup Holsting, J., San José Estépar, R. (2020). Multi-cavity Heart Segmentation in Non-contrast Non-ECG Gated CT Scans with F-CNN. In: Petersen, J., et al. Thoracic Image Analysis. TIA 2020. Lecture Notes in Computer Science(), vol 12502. Springer, Cham. https://doi.org/10.1007/978-3-030-62469-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-62469-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62468-2
Online ISBN: 978-3-030-62469-9
eBook Packages: Computer ScienceComputer Science (R0)
