17664
2018
eng
1311
5
18
article
1
2019-02-14
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Body weight estimation for dose-finding and health monitoring of lying, standing and walking patients based on RGB-D data
This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients.
Sensors
10.3390/s18051311
urn:nbn:de:bvb:20-opus-176642
Sensors 2018, 18(5):1311. DOI: 10.3390/s18051311
false
true
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Christian Pfitzner
Stefan May
Andreas Nüchter
eng
uncontrolled
RGB-D
eng
uncontrolled
human body weight
eng
uncontrolled
image processing
eng
uncontrolled
kinect
eng
uncontrolled
machine learning
eng
uncontrolled
perception
eng
uncontrolled
segmentation
eng
uncontrolled
sensor fusion
eng
uncontrolled
stroke
eng
uncontrolled
thermal camera
Datenverarbeitung; Informatik
Inzidenz und Prävention von Krankheiten
open_access
Institut für Informatik
Förderzeitraum 2018
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/17664/Pfitzner_Sensors.pdf
25916
2021
eng
27
1
21
article
1
2022-03-03
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Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI
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.
BMC Medical Imaging
10.1186/s12880-021-00551-1
urn:nbn:de:bvb:20-opus-259169
publish
BMC Medical Imaging (2021) 21:27. doi:10.1186/s12880-021-00551-1
false
true
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Markus J. Ankenbrand
Liliia Shainberg
Michael Hock
David Lohr
Laura M. Schreiber
eng
uncontrolled
deep learning
eng
uncontrolled
neural networks
eng
uncontrolled
cardiac magnetic resonance
eng
uncontrolled
sensitivity analysis
eng
uncontrolled
transformations
eng
uncontrolled
augmentation
eng
uncontrolled
segmentation
Datenverarbeitung; Informatik
Biowissenschaften; Biologie
Medizin und Gesundheit
open_access
Deutsches Zentrum für Herzinsuffizienz (DZHI)
Förderzeitraum 2021
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/25916/s12880-021-00551-1.pdf