Application of magnetic resonance sounding to tunnels for advanced detection of water-related disasters: A case study in the Dadushan Tunnel, Guizhou, China

2019 ◽  
Vol 84 ◽  
pp. 364-372 ◽  
Author(s):  
Shengwu Qin ◽  
Zhongjun Ma ◽  
Chuandong Jiang ◽  
Jun Lin ◽  
Mingzhou Bai ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Sounding

Application of integrated magnetic resonance sounding and resistivity methods for borehole implementation. A case study in Cambodia

2002 ◽  
Vol 50 (1-2) ◽  
pp. 67-81 ◽  
Author(s):  
Jean-Michel Vouillamoz ◽  
Marc Descloitres ◽  
Jean Bernard ◽  
Pierre Fourcassier ◽  
Laurent Romagny
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Sounding

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.

Use of magnetic resonance imaging to identify the edge of a dural tear in an infant with growing skull fracture: a case study

2012 ◽  
Vol 28 (11) ◽  
pp. 1951-1954 ◽  
Author(s):  
Hideki Matsuura ◽  
Shinichi Omama ◽  
Yuki Yoshida ◽  
Shunrou Fujiwara ◽  
Takayuki Honda ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Skull Fracture ◽  
Dural Tear ◽  
Resonance Imaging ◽  
Growing Skull Fracture

Open Source Medical Devices for Innovation, Education and Global Health: Case Study of Open Source Magnetic Resonance Imaging

2018 ◽  
pp. 147-163
Author(s):  
Lukas Winter ◽  
Ruben Pellicer-Guridi ◽  
Lionel Broche ◽  
Simone A. Winkler ◽  
Henning M. Reimann ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Global Health ◽  
Open Source ◽  
Medical Devices ◽  
Resonance Imaging
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