Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence
Motivation: Manual segmentation of cardiac MRI images is a time-consuming and laborious task prone to observer bias. Automatic segmentation approaches provide poor results in extreme slices. A slice classification step applied before automatic segmentation will lead to better results and reduced variability.
Goal(s): To develop a classifier model with high classification performance on short-axis(SA) cine MRI images for slice selection.
Approach: We trained and compared 2 CNN models for classifying SA cine MRI images into Apical-to-Basal vs Extreme slices.
Results: Xception model had better classification accuracy (0.90) and F1- score (0.93) when compared to InceptionV3 (0.87 and 0.89, respectively).
Impact: The proposed model will provide automatic, fast and accurate classification of MRI cine images, which will improve the accuracy of automatic segmentation of myocardium and its assessment.
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