Abstract
Cardiac magnetic resonance imaging (CMR) is a powerful non-invasive tool for diagnosing a variety of cardiovascular diseases. However, the quality of CMR imaging is susceptible to respiratory motion artifacts. Recently, an extreme cardiac MRI analysis challenge was organized to assess the effects of respiratory motion on CMR imaging quality and develop a robust segmentation framework under different levels of respiratory motion. In this paper, we have presented two different deep learning frameworks for CMR imaging quality assessment and automatic segmentation. First, we have developed 3D-DenseNet to assess the image quality, followed by 3D-deep supervision UNet with the residual module using pseudo labelling for automatic segmentation task. Experiments on the Challenge dataset showed that 3D ResNet with deep supervision using Pseudo Labeling with nnUNet achieved significantly better performance (8.747 LV, 3.787 MYO, and 5.942 RV) HD95 score than 3D-UNet.
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
Karamitsos, T.D., Francis, J.M., Myerson, S., Selvanayagam, J.B., Neubauer, S.: The role of cardiovascular magnetic resonance imaging in heart failure. J. Am. College Cardiol. 54(15), 1407–1424 (2009)
Peng, P., Lekadir, K, Gooya, A., Shao, L., Petersen, S.E., Frangi, A.F.: A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magn. Reson. Mater. Phys., Biol. Med. 29(2), 155–195 (2016)
Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?. IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018)
Lalande, A., et al.: Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge. Med. Image Anal. 79, 102428 (2022)
Mazher, M., Qayyum, A., Benzinou, A., Abdel-Nasser, M., Puig, D.: Multi-disease, Multi-view and Multi-center Right Ventricular Segmentation in Cardiac MRI Using Efficient Late-Ensemble Deep Learning Approach. In: International Workshop on Statistical Atlases and Computational Models of the Heart, pp. 335–343. Springer, Cham, 2021, September
Chen, Z., et al.: Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI. Comput. Med. Imaging Graph. 95, 102014 (2022)
Qayyum, A., Mazhar, M., Razzak, I., Bouadjenek, M.R.: Multilevel depth-wise context attention network with atrous mechanism for segmentation of covid19 affected regions. Neural Computing and Applications, pp. 1–13 (2021)
Ma, J., et al.: Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge. Med. Image Anal., 102616 (2022)
Payette, K., et al.: Fetal brain tissue annotation and segmentation challenge results. arXiv preprint arXiv:2204.09573 (2022)
Kausar, A., Razzak, I., Shapiai, I., Alshammari, R.: An improved dense V-network for fast and precise segmentation of left atrium. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (July 2021)
Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Alassafi, M.O.: Brain tumor classification based on fine-tuned models and the ensemble method. Comput., Mater. Contin. 67(3), 3967–3982 (2021)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a selfconfiguring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Wang, S., et al.: The extreme cardiac MRI analysis challenge under respiratory motion (CMRxMotion). arXiv preprint arXIv: 2210.06385 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Qayyum, A., Mazher, M., Niederer, S., Meriaudeau, F., Razzak, I. (2022). Automatic Cardiac Magnetic Resonance Respiratory Motions Assessment and Segmentation. In: Camara, O., et al. Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers. STACOM 2022. Lecture Notes in Computer Science, vol 13593. Springer, Cham. https://doi.org/10.1007/978-3-031-23443-9_46
Download citation
DOI: https://doi.org/10.1007/978-3-031-23443-9_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23442-2
Online ISBN: 978-3-031-23443-9
eBook Packages: Computer ScienceComputer Science (R0)
