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Fully automated waist circumference measurement on abdominal CT: Comparison with manual measurements and potential value for identifying overweight and obesity as an adjunct output of CT scan
2021
PLoS ONE
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Mid-waist WCs were automatically measured on noncontrast axial CT images using a deep learning-based body segmentation algorithm. ...
Objective Waist circumference (WC) is a widely accepted anthropometric parameter of central obesity. ...
In recent years, deep learning-based algorithms have increasingly been investigated for automated segmentation of body composition from medical imaging [14, 15] . ...
doi:10.1371/journal.pone.0254704
pmid:34280224
pmcid:PMC8289071
fatcat:cnbf6oee5jbw5owc54ordsxsam
Height Estimation of Children under Five Years using Depth Images
[article]
2021
arXiv
pre-print
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In this work, we propose a CNN-based approach to estimate the height of standing children under five years from depth images collected using a smart-phone. ...
Detecting malnutrition requires anthropometric measurements of weight, height, and middle-upper arm circumference. ...
This led us to transform the point cloud data to depth images, which was used to train a Convolutional Neural Network (CNN) based deep learning model for height estimation. ...
arXiv:2105.01688v2
fatcat:6ylprbfu6fbkpim7fv3mzw2eam
Anthropometric clothing measurements from 3D body scans
2020
Machine Vision and Applications
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We propose a full processing pipeline to acquire anthropometric measurements from 3D measurements. The first stage of our pipeline is a commercial point cloud scanner. ...
We scanned 194 male and 181 female subjects, and the proposed pipeline provides mean absolute errors from 2.5 to 16.0 mm depending on the anthropometric measurement. August 2017. ...
points E d (X ) = v i ∈V w i dist 2 (T scan , X i v i ) (3) where X i is a linear mapping of a single model vertex v i to correspondence in T scan . w i defines whether a model point has a correspondence ...
doi:10.1007/s00138-019-01054-4
fatcat:22m2qetmkjchzmicr3lzosbe7m
Artificial intelligence and abdominal adipose tissue analysis: a literature review
2021
Quantitative Imaging in Medicine and Surgery
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AI holds the potential to extract quantitative data from computed tomography (CT) and magnetic resonance (MR) images, which in most of the cases are acquired for other purposes. ...
On the other hand, quantitative imaging analysis based on artificial intelligence (AI) has been proposed as a fast and reliable automatic technique for segmentation of abdominal adipose tissue compartments ...
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved ...
doi:10.21037/qims-21-370
pmid:34603998
pmcid:PMC8408793
fatcat:4ymzwcw36jdqhdmxhj2usudodm
body2vec: 3D Point Cloud Reconstruction for Precise Anthropometry with Handheld Devices
2020
Journal of Imaging
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Current point cloud extraction methods based on photogrammetry generate large amounts of spurious detections that hamper useful 3D mesh reconstructions or, even worse, the possibility of adequate measurements ...
Applying our method to the raw videos significantly enhanced the quality of the results of the point cloud as compared with the LiDAR-based mesh, and of the anthropometric measurements as compared with ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/jimaging6090094
pmid:34460751
fatcat:suvrffa3mbgrjhuledhvgulgom
MACHINE LEARNING FOR APPROXIMATING UNKNOWN FACE
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The paper presents a deep learning approach for facial approximation basing on a skull. ...
approximation based on skull digital 3D model with deep learning techniques. ...
The estimations generated from the given skull visually match well with the skin surface extracted from the CT scan. ...
doi:10.5194/isprs-archives-xliii-b2-2020-857-2020
fatcat:s2rscqb525cfbivro3itakreia
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics
2016
IEEE Transactions on Information Forensics and Security
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In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from image and video data. ...
Recent research has explored the possibility of extracting ancillary information from primary biometric traits, viz., face, fingerprints, hand geometry and iris. ...
[98] , where a detailed 3D human body shape in the presence of clothes was modeled based on a space of human shapes, learned from a large database of registered body scans. ...
doi:10.1109/tifs.2015.2480381
fatcat:rmgdjjvbznchhdeib24bfzonki
Estimation of Spectral Notches from Pinna Meshes: Insights from a Simple Computational Model
2021
IEEE/ACM Transactions on Audio Speech and Language Processing
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In this paper we propose a simple computational model able to predict the center frequencies of pinna notches from ear meshes. ...
the human pinna is still a topic of debate. ...
The lack of large and standardized HRTF datasets together with the relevant anthropometric data also complicates the use of machine learning and deep learning techniques, although some research in that ...
doi:10.1109/taslp.2021.3101928
fatcat:koujilbanfdcdakvd4mphjbd4q
Novel Anthropometry Based on 3D-Bodyscans Applied to a Large Population Based Cohort
2016
PLoS ONE
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We analyzed 3D whole body scanning data of nearly 10,000 participants of a cohort collected from the adult population of Leipzig, one of the largest cities in Eastern Germany. ...
Three-dimensional (3D) whole body scanners are increasingly used as precise measuring tools for the rapid quantification of anthropometric measures in epidemiological studies. ...
We acknowledge support from the German Research Foundation (DFG) and Universität Leipzig within the program of Open Access Publishing. ...
doi:10.1371/journal.pone.0159887
pmid:27467550
pmcid:PMC4965021
fatcat:by6o4of2b5evtniob45zhtvfge
Coupling Top-down and Bottom-up Methods for 3D Human Pose and Shape Estimation from Monocular Image Sequences
[article]
2014
arXiv
pre-print
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Additionally, these learned priors can be actively adapted to the test data using a likelihood based feedback mechanism. ...
learned regression models to sustain multimodality of the posterior during tracking. ...
Part circumferences computed from the estimated 3D shapes are compared against groundtruth measurements estimated from laser scan shape of the same subject accuracy; the hips in particular often have deep ...
arXiv:1410.0117v2
fatcat:db73dqqopjabbiuphnv2gk5hhi
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies
[article]
2020
arXiv
pre-print
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years; 152 women) from the German National Cohort (NAKO) database for model training, validation, and testing with a transfer learning between the cohorts. ...
Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions. ...
Acknowledgements The work was supported in part by a grant (01GI0925) from the German Federal Ministry of Education ...
arXiv:2008.02251v1
fatcat:egrvlizw6fccdkvfgdaflubcju
Automatic Detection and Classification of Human Knee Osteoarthritis Using Convolutional Neural Networks
2022
Computers Materials & Continua
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In this research, a new automatic classification of KOA images based on unsupervised local center of mass (LCM) segmentation method and deep Siamese Convolutional Neural Network (CNN) is presented. ...
Clinically, KOA is classified into four grades ranging from 1 to 4 based on the degradation of the ligament in between these two bones and causes suffering from impaired movement. ...
Feature Extraction from Segmented ROI 3.3.1 First Order Statistics Based Approach-Histogram Based Features First-order statistics measures are calculated from the original image values and don't consider ...
doi:10.32604/cmc.2022.020571
fatcat:e7k76z444vgbnksjnjlguv3ojq
AI-Driven quantification, staging and outcome prediction of COVID-19 pneumonia
2020
Medical Image Analysis
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Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing ...
In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. ...
DIC20161236437), the Swiss National Science Foundation Grant no. 188153 and benefited from methodological developments done in the context of Dr. ...
doi:10.1016/j.media.2020.101860
pmid:33171345
pmcid:PMC7558247
fatcat:iz5zcq7ftzg7lendm563asl7fu
The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions
2020
Nature Communications
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UK Biobank is a population-based cohort of half a million participants aged 40-69 years recruited between 2006 and 2010. ...
This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions. ...
first like to acknowledge all UK Biobank participants for not only generously dedicating their free time to participate, but for also maintaining contact over many years that has made an imaging study of ...
doi:10.1038/s41467-020-15948-9
pmid:32457287
pmcid:PMC7250878
fatcat:752g4x4knrdijchif4eljgaeoq
Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach
2020
Diagnostics
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In total, 174 measurements from 80 subjects were recorded. ...
Our screening software performs image processing and machine learning-based estimation related to the deformity indices of the cranial ratio (CR) and cranial vault asymmetry index (CVAI) to determine the ...
In all the measurements, an operator scanned the patient's head, which was covered with a cap. ...
doi:10.3390/diagnostics10070495
pmid:32707742
pmcid:PMC7400331
fatcat:cn4b2fu5onbz5dfodzwa3v2y3u
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