Abstract
In this work, we propose solutions for the two tasks of the CMRxMotion challenge; 1) quality control and 2) image segmentation in the presence of respiratory motion artifacts. We develop a k-space based motion simulation approach to generate cardiac MR images with respiratory motion artifacts on open-source artifact-free data to handle data scarcity. For task 1, a motion-denoising auto-encoder is trained to reconstruct motion-free images from the pairs of images with and without simulated motion. The encoder part of the auto-encoder is used as a feature extractor for a fully-connected classifier. For task 2, an ensemble of modified 2D nn-Unet models is proposed to tackle different aspects of variations in the data with the purpose of improving the robustness of the model to images hampered by respiratory motion artifacts. All proposed models in this paper are trained using the images with simulated motion artifacts. The proposed quality control model achieves a classification accuracy of 0.75 with the Cohen’s kappa coefficient of 0.64 and the ensemble model obtains the mean Dice scores of 0.922, 0.829, and 0.910 respectively for the left ventricle, myocardium, and right ventricle segmentation on the validation set of the CMRxMotion challenge.
S. Amirrajab, Y. Al Khalil and C. M. Scannell—Contributed equally.
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Abraham, N., Khan, N.M.: A novel focal Tversky loss function with improved attention U-Net for lesion segmentation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 683–687. IEEE (2019)
Campello, V.M., et al.: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M &Ms challenge. IEEE Trans. Med. Imaging 40(12), 3543–3554 (2021)
Ferreira, P.F., Gatehouse, P.D., Mohiaddin, R.H., Firmin, D.N.: Cardiovascular magnetic resonance artefacts. J. Cardiovasc. Magn. Reson. 15(1), 1–39 (2013)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: NNU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Lorch, B., Vaillant, G., Baumgartner, C., Bai, W., Rueckert, D., Maier, A.: Automated detection of motion artefacts in MR imaging using decision forests. J. Med. Eng. 2017 (2017)
Meissen, F., Wiestler, B., Kaissis, G., Rueckert, D.: On the pitfalls of using the residual error as anomaly score (2022). https://doi.org/10.48550/ARXIV.2202.03826. https://arxiv.org/abs/2202.03826
Oksuz, I., et al.: Automatic CNN-based detection of cardiac MR motion artefacts using K-space data augmentation and curriculum learning. Med. Image Anal. 55, 136–147 (2019)
Shaw, R., Sudre, C.H., Varsavsky, T., Ourselin, S., Cardoso, M.J.: A K-space model of movement artefacts: application to segmentation augmentation and artefact removal. IEEE Trans. Med. Imaging 39(9), 2881–2892 (2020)
Wang, S., et al.: The extreme cardiac MRI analysis challenge under respiratory motion (CMRxMotion) (2022). https://arxiv.org/abs/2210.06385
Acknowledgments
This research is a part of the openGTN project, supported by the European Union in the Marie Curie Innovative Training Networks (ITN) fellowship program under project No. 764465.
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Amirrajab, S., Al Khalil, Y., Pluim, J., Breeuwer, M., Scannell, C.M. (2022). Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifacts Using Simulated Data. 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_44
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DOI: https://doi.org/10.1007/978-3-031-23443-9_44
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