Edge-preserving image compression for magnetic-resonance images using dynamic associative neural networks (DANN)-based neural networks

1994 ◽  
Author(s):  
Tat C. Wan ◽  
Mansur R. Kabuka
Keyword(s):  
Neural Networks ◽  
Magnetic Resonance ◽  
Image Compression ◽  
Magnetic Resonance Images ◽  
Edge Preserving

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.

scholarly journals Sensitivity Analysis for Interpretation of Machine Learning Based Segmentation Models in Cardiac MRI

2020 ◽  
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 ◽  
Training Set ◽  
Black Boxes ◽  
Common Task ◽  
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. Results We show that sensitivity analysis is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We also provide an open source Python library (misas), that facilitates the use of this method with arbitrary data and models. By enabling a better understanding of neural networks through sensitivity analysis it also assists in decision making. We demonstrate this in two case studies on cardiac magnetic resonance imaging.Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox with a new tool that makes segmentation models more interpretable. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

scholarly journals Comparative Study of Encoder–Decoder-Based Convolutional Neural Networks in Cartilage Delineation from Knee Magnetic Resonance Images

2020 ◽  
Vol 16 ◽  
Author(s):  
Ching Wai Yong ◽  
Khin Wee Lai ◽  
Belinda Pingguan Murphy ◽  
Yan Chai Hum
Keyword(s):  
Neural Networks ◽  
Magnetic Resonance ◽  
Convolutional Neural Networks ◽  
Soft Tissues ◽  
Early Stage ◽  
Joint Inflammation ◽  
Magnetic Resonance Images ◽  
Mr Images ◽  
Daily Lives ◽  
Osteoarthritis Initiative

Background: Osteoarthritis (OA) is a common degenerative joint inflammation which may lead to disability. Although OA is not lethal, this disease will remarkably affect patient’s mobility and their daily lives. Detecting OA at an early stage allows for early intervention and may slow down disease progression. Introduction: Magnetic resonance imaging is a useful technique to visualize soft tissues within the knee joint. Cartilage delineation in magnetic resonance (MR) images helps in understanding the disease progressions. Convolutional neural networks (CNNs) have shown promising results in computer vision tasks, and various encoder–decoder-based segmentation neural networks are introduced in the last few years. However, the performances of such networks are unknown in the context of cartilage delineation. Methods: This study trained and compared 10 encoder–decoder-based CNNs in performing cartilage delineation from knee MR images. The knee MR images are obtained from Osteoarthritis Initiative (OAI). The benchmarking process is to compare various CNNs based on the physical specifications and segmentation performances. Results: LadderNet has the least trainable parameters with model size of 5 MB. UNetVanilla crowned the best performances by having 0.8369, 0.9108, and 0.9097 on JSC, DSC, and MCC. Conclusion: UNetVanilla can be served as a benchmark for cartilage delineation in knee MR images while LadderNet served as alternative if there are hardware limitations during production.

KIKI ‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images

2018 ◽  
Vol 80 (5) ◽  
pp. 2188-2201 ◽  
Author(s):  
Taejoon Eo ◽  
Yohan Jun ◽  
Taeseong Kim ◽  
Jinseong Jang ◽  
Ho‐Joon Lee ◽  
...  
Keyword(s):  
Neural Networks ◽  
Magnetic Resonance ◽  
Convolutional Neural Networks ◽  
Magnetic Resonance Images ◽  
Cross Domain
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