Abstract
Cardiac magnetic resonance imaging (MRI) may suffer from motion-related artifacts resulting in non-diagnostic quality images. Therefore, image quality assessment (IQA) is essential for the cardiac MRI analysis. The CMRxMotion challenge aims to develop automatic methods for IQA. In this paper, given the limited amount of training data, we designed three special data augmentation techniques to enlarge the dataset and to balance the class ratio. The generated dataset was used to pre-train the model. We then randomly selected two multi-channel 2D images from one 3D volume to mimic sample inspection and introduced ResNet as the backbone to extract features from those two 2D images. Meanwhile, a channel-based attention module was used to fuse the features for the classification. Our method achieved a mean accuracy of 0.75 and 0.725 in 4-fold cross validation and the held-out validation dataset, respectively. The code can be found here (https://github.com/xsunn/CMRxMotion).
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
Similar content being viewed by others
References
Krupa, K., Bekiesińska-Figatowska, M.: Artifacts in magnetic resonance imaging. Pol. J. Radiol. 80, 93 (2015)
Zhang, L., et al.: Automated quality assessment of cardiac MR images using convolutional neural networks. In: International Workshop on Simulation and Synthesis in Medical Imaging. Springer, Cham (2016)
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Bosse, S., et al.: Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans. Image Process. 27(1), 206–219 (2017)
Su, S., et al.: Blindly assess image quality in the wild guided by a self-adaptive hyper network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Ma, J.J., et al.: Diagnostic image quality assessment and classification in medical imaging: Opportunities and challenges. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE (2020)
Fu, H., et al.: Evaluation of retinal image quality assessment networks in different color-spaces. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham (2019)
Oksuz, I., et al.: Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham (2019)
Lyu, Q., et al.: Cine cardiac MRI motion artifact reduction using a recurrent neural network. IEEE Trans. Med. Imaging 40(8), 2170–2181 (2021)
Wang, Q., et al.: ECA-Net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Seattle, WA, USA (2020)
Zhang, Y., et al.: Semi-supervised cardiac image segmentation via label propagation and style transfer. In: International Workshop on Statistical Atlases and Computational Models of the Heart. Springer, Cham (2020)
Wang, L., Lee, C.Y., Tu, Z., Lazebnik, S.: Training deeper convolutional networks with deep supervision. arXiv preprint arXiv:1505.02496 (2015)
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
Sun, X., Cheng, LH., van der Geest, R.J. (2022). Combination Special Data Augmentation and Sampling Inspection Network for Cardiac Magnetic Resonance Imaging Quality Classification. 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_45
Download citation
DOI: https://doi.org/10.1007/978-3-031-23443-9_45
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)
