Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence
Motivation: Deep learning for myocardial scar segmentation offers an alternative to time-consuming and observer-dependent semi-automatic approaches.
Goal(s): The objective of this study was to assess the impact of effective image resolution on neural network training for ventricular scar segmentation.
Approach: Convolutional neural networks were trained on magnetic resonance images with constant matrix size and field-of-view but differing resolutions, and tested on a range of resolutions to investigate the effects.
Results: Neural networks trained on a specific resolution indicated a bias of the scar area estimation when employed to lower -or higher-resolution images. Deploying a network trained on multiple resolutions resulted in reduced resolution dependency.
Impact: The effective image resolution, with constant matrix size and field-of-view, should be considered when training a segmentation model to alleviate unwanted bias in the estimation. Training on multiple resolutions has been shown to increase network precision and robustness.
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