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).
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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
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DOI: https://doi.org/10.1007/978-3-031-23443-9_45
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