Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI

• URL: http://arxiv.org/abs/2210.07717v1
• Date: Fri, 14 Oct 2022 11:31:40 GMT
• Title: Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI
• Authors: Ruizhe Li, Xin Chen
• Abstract summary: We propose an automatic cardiac MRI quality estimation framework using ensemble and transfer learning. Multiple pre-trained models were initialised and fine-tuned on 2-dimensional image patches sampled from the training data. It achieved a classification accuracy of 78.8% and 70.0% on the training set and validation set, respectively.
• Score: 5.186000805926489
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cardiac Magnetic Resonance Imaging (MRI) plays an important role in the analysis of cardiac function. However, the acquisition is often accompanied by motion artefacts because of the difficulty of breath-hold, especially for acute symptoms patients. Therefore, it is essential to assess the quality of cardiac MRI for further analysis. Time-consuming manual-based classification is not conducive to the construction of an end-to-end computer aided diagnostic system. To overcome this problem, an automatic cardiac MRI quality estimation framework using ensemble and transfer learning is proposed in this work. Multiple pre-trained models were initialised and fine-tuned on 2-dimensional image patches sampled from the training data. In the model inference process, decisions from these models are aggregated to make a final prediction. The framework has been evaluated on CMRxMotion grand challenge (MICCAI 2022) dataset which is small, multi-class, and imbalanced. It achieved a classification accuracy of 78.8% and 70.0% on the training set (5-fold cross-validation) and a validation set, respectively. The final trained model was also evaluated on an independent test set by the CMRxMotion organisers, which achieved the classification accuracy of 72.5% and Cohen's Kappa of 0.6309 (ranked top 1 in this grand challenge). Our code is available on Github: https://github.com/ruizhe-l/CMRxMotion.

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