Abstract
This paper proposes a fully-automated technique for estimation of an antenatal risk score for Coarctation of the Aorta (CoA) from fetal T2-weighted 3D cardiac magnetic resonance imaging (CMR). Our framework combines automated multi-class fetal cardiac vessel segmentation based on two fully-labelled atlases (control and CoA) with statistical shape analysis of the fetal arch. The segmentation framework is weakly-supervised, requiring only condition-specific atlas labels which are propagated to training subjects. The proposed shape analysis method utilizes the predicted segmentation to extract a set of centerlines and radii capturing the shape of the fetal arch. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are then applied to derive a CoA risk score. The segmentation framework achieves a mean Dice of \(0.86 \pm 0.03\) for the aortic region. The CoA shape biomarker accurately discriminated between false positives (FP) and CoA cases (AUC 0.93) and showed good generalisability in an independent test set (AUC 0.87), achieving comparable performance to approaches using manual segmentations. Our proposed fully-automatic technique has the potential to improve the antenatal diagnosis of CoA from 3D fetal CMR data.
P. Ramirez and U. Hermida—The first two authors contributed equally.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Familiari, A., et al.: Risk factors for coarctation of the aorta on prenatal ultrasound: a systematic review and meta-analysis. Circulation 135(8), 772–785 (2017)
Grigorescu, I., et al.: Diffusion tensor driven image registration: a deep learning approach. In: Špiclin, Ž, McClelland, J., Kybic, J., Goksel, O. (eds.) WBIR 2020. LNCS, vol. 12120, pp. 131–140. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50120-4_13
Hermida, U., et al.: Learning the hidden signature of fetal arch anatomy: a three-dimensional shape analysis in suspected coarctation of the aorta. J. Cardiovasc. Transl. Res. 16, 738–747 (2022)
Hermida, U., et al.: Simplifying disease staging models into a single anatomical axis-a case study of aortic coarctation in-utero. In: International Workshop on Statistical Atlases and Computational Models of the Heart, pp. 269–279. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-23443-9_25
Kainz, B., et al.: Fast volume reconstruction from motion corrupted stacks of 2d slices. IEEE Trans. Med. Imaging 34(9), 1901–1913 (2015)
Kuklisova-Murgasova, M., Quaghebeur, G., Rutherford, M.A., Hajnal, J.V., Schnabel, J.A.: Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med. Image Anal. 16(8), 1550–1564 (2012)
Law, M.A., Tivakaran, V.S.: Coarctation of the aorta. In: StatPearls [Internet]. StatPearls Publishing (2022)
Lloyd, D.F., et al.: Three-dimensional visualisation of the fetal heart using prenatal MRI with motion-corrected slice-volume registration: a prospective, single-centre cohort study. Lancet 393(10181), 1619–1627 (2019)
Lloyd, D.F., et al.: Analysis of 3-dimensional arch anatomy, vascular flow, and postnatal outcome in cases of suspected coarctation of the aorta using fetal cardiac magnetic resonance imaging. Circ. Cardiovasc. Imaging 14(7), e012411 (2021)
Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Ramirez Gilliland, P., et al.: Automated multi-class fetal cardiac vessel segmentation in aortic arch anomalies using t2-weighted 3D fetal MRI. In: International Workshop on Preterm, Perinatal and Paediatric Image Analysis, pp. 82–93. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-17117-8_8
Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 379–387. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_44
Uus, A., et al.: Deformable slice-to-volume registration for motion correction of fetal body and placenta MRI. IEEE Trans. Med. Imaging 39(9), 2750–2759 (2020)
Uus, A.U., et al.: Automated 3D reconstruction of the fetal thorax in the standard atlas space from motion-corrupted MRI stacks for 21–36 weeks ga range. Med. Image Anal. 80, 102484 (2022). https://doi.org/10.1016/j.media.2022.102484. https://www.sciencedirect.com/science/article/pii/S1361841522001311
Uus, A.U., et al.: 3D black blood cardiovascular magnetic resonance atlases of congenital aortic arch anomalies and the normal fetal heart: application to automated multi-label segmentation. J. Cardiovasc. Magn. Reson. 24(1), 1–13 (2022)
Varela, M., et al.: Novel computational analysis of left atrial anatomy improves prediction of atrial fibrillation recurrence after ablation. Front. Physiol. 8(FEB), 68 (2017). https://doi.org/10.3389/fphys.2017.00068
Xu, Z., Niethammer, M.: DeepAtlas: joint semi-supervised learning of image registration and segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 420–429. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_47
Zhao, A., Balakrishnan, G., Durand, F., Guttag, J.V., Dalca, A.V.: Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8543–8553 (2019)
Acknowledgements
We would like to acknowledge funding from the EPSRC Centre for Doctoral Training in Smart Medical Imaging (EP/S022104/1).
We thank everyone who was involved in the acquisition and examination of the datasets and all participating mothers. This work was supported by the Rosetrees Trust [A2725], the Wellcome/EPSRC Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z], the Wellcome Trust and EPSRC IEH award [102431] for the iFIND project, the NIHR Clinical Research Facility (CRF) at Guy’s and St Thomas’ and by the National Institute for Health Research Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
All fetal MRI datasets used in this work were processed subject to informed consent of the participants [REC: 07/H0707/105; REC: 14/LO/1806].
The work follows appropriate ethical standards in conducting research and writing the manuscript, following all applicable laws and regulations regarding treatment of animals or human subjects.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ramirez, P. et al. (2023). Towards Automatic Risk Prediction of Coarctation of the Aorta from Fetal CMR Using Atlas-Based Segmentation and Statistical Shape Modelling. In: Link-Sourani, D., Abaci Turk, E., Macgowan, C., Hutter, J., Melbourne, A., Licandro, R. (eds) Perinatal, Preterm and Paediatric Image Analysis. PIPPI 2023. Lecture Notes in Computer Science, vol 14246. Springer, Cham. https://doi.org/10.1007/978-3-031-45544-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-45544-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45543-8
Online ISBN: 978-3-031-45544-5
eBook Packages: Computer ScienceComputer Science (R0)