scholarly journals Correction to: Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

2020 ◽  
Author(s):  
Sven Koitka ◽  
Lennard Kroll ◽  
Eugen Malamutmann ◽  
Arzu Oezcelik ◽  
Felix Nensa
Keyword(s):  
Neural Networks ◽  
Body Composition ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Ct Imaging ◽  
Original Version ◽  
Composition Analysis ◽  
Body Composition Analysis

The original version of this article, published on 18 September 2020, unfortunately contained a mistake.

scholarly journals Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

2020 ◽  
Author(s):  
Sven Koitka ◽  
Lennard Kroll ◽  
Eugen Malamutmann ◽  
Arzu Oezcelik ◽  
Felix Nensa
Keyword(s):  
Neural Networks ◽  
Body Composition ◽  
Correlation Coefficients ◽  
Ct Imaging ◽  
Body Tissue ◽  
Composition Analysis ◽  
Tissue Composition ◽  
Intra Class Correlation ◽  
Body Composition Analysis ◽  
Body Regions

Abstract Objectives Body tissue composition is a long-known biomarker with high diagnostic and prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible, and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. Methods Therefore, an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known Hounsfield unit limits. Results The Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. Conclusions Our results show that fully automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analyzing body composition in the clinical routine. Key Points • Our study enables fully automated body composition analysis on routine abdomen CT scans. • The best segmentation models for semantic body region segmentation achieved an averaged Sørensen Dice score of 0.9553. • Subclassified tissue volumes achieved intra-class correlation coefficients over 0.99.

Ensembled artificial neural networks to predict the fitness score for body composition analysis

2010 ◽  
Vol 15 (5) ◽  
pp. 341-348 ◽  
Author(s):  
X. R. Cui ◽  
M. F. Abbod ◽  
Q. Liu ◽  
Jiann-Shing Shieh ◽  
T. Y. Chao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Body Composition ◽  
Artificial Neural Networks ◽  
Composition Analysis ◽  
Body Composition Analysis ◽  
Fitness Score ◽  
Artificial Neural

OR35: Automated Body Composition Analysis of Computed Tomography Scans Using Neural Networks

2019 ◽  
Vol 38 ◽  
pp. S17
Author(s):  
M.T. Paris ◽  
D.K. Heyland ◽  
P. Tandon ◽  
H.F. Furberg ◽  
T. Premji ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Body Composition ◽  
Composition Analysis ◽  
Body Composition Analysis ◽  
Computed Tomography Scans

scholarly journals Automated body composition analysis of clinically acquired computed tomography scans using neural networks

2020 ◽  
Vol 39 (10) ◽  
pp. 3049-3055 ◽  
Author(s):  
Michael T. Paris ◽  
Puneeta Tandon ◽  
Daren K. Heyland ◽  
Helena Furberg ◽  
Tahira Premji ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Body Composition ◽  
Composition Analysis ◽  
Body Composition Analysis ◽  
Computed Tomography Scans

scholarly journals Semantic Segmentation of Cerebellum in 2D Fetal Ultrasound Brain Images using Convolutional Neural Networks

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Vishal Singh ◽  
Pradeeba Sridar ◽  
Jinman Kim ◽  
Ralph Nanan ◽  
N. Poornima ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Fetal Ultrasound ◽  
Brain Images

scholarly journals Convolutional Neural Networks for Semantic Segmentation as a Tool for Multiclass Face Analysis in Thermal Infrared

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
David Müller ◽  
Andreas Ehlen ◽  
Bernd Valeske
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Thermal Infrared ◽  
Relative Temperature ◽  
Face Analysis ◽  
Image Translation ◽  
The Face ◽  
Live Image ◽  
Quantitative Thermography

AbstractConvolutional neural networks were used for multiclass segmentation in thermal infrared face analysis. The principle is based on existing image-to-image translation approaches, where each pixel in an image is assigned to a class label. We show that established networks architectures can be trained for the task of multiclass face analysis in thermal infrared. Created class annotations consisted of pixel-accurate locations of different face classes. Subsequently, the trained network can segment an acquired unknown infrared face image into the defined classes. Furthermore, face classification in live image acquisition is shown, in order to be able to display the relative temperature in real-time from the learned areas. This allows a pixel-accurate temperature face analysis e.g. for infection detection like Covid-19. At the same time our approach offers the advantage of concentrating on the relevant areas of the face. Areas of the face irrelevant for the relative temperature calculation or accessories such as glasses, masks and jewelry are not considered. A custom database was created to train the network. The results were quantitatively evaluated with the intersection over union (IoU) metric. The methodology shown can be transferred to similar problems for more quantitative thermography tasks like in materials characterization or quality control in production.

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