Super-resolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks

2020 ◽  
Vol 80 ◽  
pp. 101698 ◽  
Author(s):  
Ming Zhao ◽  
Xinhong Liu ◽  
Hui Liu ◽  
Kelvin K.L. Wong
Keyword(s):  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Super Resolution ◽  
Magnetic Resonance Images ◽  
Generative Adversarial Networks ◽  
Laplacian Pyramid ◽  
Adversarial Networks

scholarly journals Generating synthetic CTs from magnetic resonance images using generative adversarial networks

2018 ◽  
Vol 45 (8) ◽  
pp. 3627-3636 ◽  
Author(s):  
Hajar Emami ◽  
Ming Dong ◽  
Siamak P. Nejad-Davarani ◽  
Carri K. Glide-Hurst
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

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