Abstract
The functional assessment of the left ventricle chamber of the heart requires detecting four landmark locations and measuring the internal dimension of the left ventricle and the approximate mass of the surrounding muscle. The key challenge of automating this task with machine learning is the sparsity of clinical labels, i.e., only a few landmark pixels in a high-dimensional image are annotated, leading many prior works to heavily rely on isotropic label smoothing. However, such a label smoothing strategy ignores the anatomical information of the image and induces some bias. To address this challenge, we introduce an echocardiogram-based, hierarchical graph neural network (GNN) for left ventricle landmark detection (EchoGLAD). Our main contributions are: 1) a hierarchical graph representation learning framework for multi-resolution landmark detection via GNNs; 2) induced hierarchical supervision at different levels of granularity using a multi-level loss. We evaluate our model on a public and a private dataset under the in-distribution (ID) and out-of-distribution (OOD) settings. For the ID setting, we achieve the state-of-the-art mean absolute errors (MAEs) of 1.46 mm and 1.86 mm on the two datasets. Our model also shows better OOD generalization than prior works with a testing MAE of 4.3 mm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)
Chen, R., Ma, Y., Chen, N., Lee, D., Wang, W.: Cephalometric landmark detection by attentive feature pyramid fusion and regression-voting. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 873–881. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_97
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021)
Duffy, G., et al.: High-throughput precision phenotyping of left ventricular hypertrophy with cardiovascular deep learning. JAMA Cardiol. 7(4), 386–395 (2022)
Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)
Gilbert, A., Holden, M., Eikvil, L., Aase, S.A., Samset, E., McLeod, K.: Automated left ventricle dimension measurement in 2D cardiac ultrasound via an anatomically meaningful CNN approach. In: Wang, Q., et al. (eds.) PIPPI/SUSI -2019. LNCS, vol. 11798, pp. 29–37. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32875-7_4
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1263–1272. JMLR.org (2017)
Goan, E., Fookes, C.: Bayesian neural networks: an introduction and survey. In: Mengersen, K.L., Pudlo, P., Robert, C.P. (eds.) Case Studies in Applied Bayesian Data Science. LNM, vol. 2259, pp. 45–87. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-42553-1_3
Goco, J.A.D., Jafari, M.H., Luong, C., Tsang, T., Abolmaesumi, P.: An efficient deep landmark detection network for PLAX EF estimation using sparse annotations. In: Linte, C.A., Siewerdsen, J.H. (eds.) Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 12034, p. 120340N. International Society for Optics and Photonics, SPIE (2022)
Gradman, A.H., Alfayoumi, F.: From left ventricular hypertrophy to congestive heart failure: Management of hypertensive heart disease. Progress Cardiovas. Dis. 48(5), 326–341 (2006). hypertension 2006 Update
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Howard, J.P., et al.: Automated left ventricular dimension assessment using artificial intelligence developed and validated by a UK-wide collaborative. Circ. Cardiovasc. Imaging 14, e011951–e011951 (2021)
Jafari, M.H., et al.: U-land: uncertainty-driven video landmark detection. IEEE Trans. Med. Imaging 41(4), 793–804 (2022)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)
Li, W., et al.: Structured landmark detection via topology-adapting deep graph learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 266–283. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_16
Lin, C., et al.: Structure-coherent deep feature learning for robust face alignment. IEEE Trans. Image Process. 30, 5313–5326 (2021)
Lin, J., et al.: Reciprocal landmark detection and tracking with extremely few annotations. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15165–15174. IEEE Computer Society (2021)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE/CVF International Conference on Computer Vision, pp. 9992–10002. IEEE Computer Society (oct 2021)
McCouat, J., Voiculescu, I.: Contour-hugging heatmaps for landmark detection. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20565–20573 (2022)
McFarland, T.M., Alam, M., Goldstein, S., Pickard, S.D., Stein, P.D.: Echocardiographic diagnosis of left ventricular hypertrophy. Circulation 50 (1978)
Mokhtari, M., Tsang, T., Abolmaesumi, P., Liao, R.: Echognn: explainable ejection fraction estimation with graph neural networks. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI. LNCS, vol. 13434, pp. 360–369. Springer, Cham (2022)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)
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
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61–80 (2009)
Sofka, M., Milletari, F., Jia, J., Rothberg, A.: Fully convolutional regression network for accurate detection of measurement points. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 258–266. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_30
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mokhtari, M. et al. (2023). EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14223. Springer, Cham. https://doi.org/10.1007/978-3-031-43901-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-43901-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43900-1
Online ISBN: 978-3-031-43901-8
eBook Packages: Computer ScienceComputer Science (R0)