TY - GEN
T1 - Combination Special Data Augmentation and Sampling Inspection Network for Cardiac Magnetic Resonance Imaging Quality Classification
AU - Sun, Xiaowu
AU - Cheng, Li Hsin
AU - van der Geest, Rob J.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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 ).
AB - 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 ).
KW - Cardiac MRI
KW - Data augmentation
KW - Image quality assessment
UR - https://www.scopus.com/pages/publications/85148031458
U2 - 10.1007/978-3-031-23443-9_45
DO - 10.1007/978-3-031-23443-9_45
M3 - Conference Proceeding
AN - SCOPUS:85148031458
SN - 9783031234422
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 476
EP - 484
BT - Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers
A2 - Camara, Oscar
A2 - Puyol-Antón, Esther
A2 - Suinesiaputra, Avan
A2 - Young, Alistair
A2 - Qin, Chen
A2 - Sermesant, Maxime
A2 - Wang, Shuo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 18 September 2022
ER -