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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

Ijin Joo, Min-Sun Kwak, Dae Hyun Park, Soon Ho Yoon, Clemens Fürnsinn
2021 PLoS ONE  
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]

Anusua Trivedi, Mohit Jain, Nikhil Kumar Gupta, Markus Hinsche, Prashant Singh, Markus Matiaschek, Tristan Behrens, Mirco Militeri, Cameron Birge, Shivangi Kaushik, Archisman Mohapatra, Rita Chatterjee (+2 others)
2021 arXiv   pre-print
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

Song Yan, Johan Wirta, Joni-Kristian Kämäräinen
2020 Machine Vision and Applications  
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

Federico Greco, Carlo Augusto Mallio
2021 Quantitative Imaging in Medicine and Surgery  
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

Magda Alexandra Trujillo-Jiménez, Pablo Navarro, Bruno Pazos, Leonardo Morales, Virginia Ramallo, Carolina Paschetta, Soledad De Azevedo, Anahí Ruderman, Orlando Pérez, Claudio Delrieux, Rolando Gonzalez-José
2020 Journal of Imaging  
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

V. A. Knyaz, V. V. Kniaz, M. M. Novikov, R. M. Galeev
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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

Antitza Dantcheva, Petros Elia, Arun Ross
2016 IEEE Transactions on Information Forensics and Security  
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

Simone Spagnol, Riccardo Miccini, Marius George Onofrei, Runar Unnthorsson, Stefania Serafin
2021 IEEE/ACM Transactions on Audio Speech and Language Processing  
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

Henry Löffler-Wirth, Edith Willscher, Peter Ahnert, Kerstin Wirkner, Christoph Engel, Markus Loeffler, Hans Binder, Dong Hoon Shin
2016 PLoS ONE  
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]

Atul Kanaujia
2014 arXiv   pre-print
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]

Thomas Küstner, Tobias Hepp, Marc Fischer, Martin Schwartz, Andreas Fritsche, Hans-Ulrich Häring, Konstantin Nikolaou, Fabian Bamberg, Bin Yang, Fritz Schick, Sergios Gatidis, Jürgen Machann
2020 arXiv   pre-print
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

Mohamed Yacin Sikkandar, S. Sabarunisha Begum, Abdulaziz A. Alkathiry, Mashhor Shlwan N. Alotaibi, Md Dilsad Manzar
2022 Computers Materials & Continua  
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

Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella, Stergios Christodoulidis, Trieu-Nghi Hoang-Thi, Severine Dangeard, Eric Deutsch, Fabrice Andre, Enora Guillo, Nara Halm, Stefany El Hajj, Florian Bompard (+23 others)
2020 Medical Image Analysis  
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

Thomas J. Littlejohns, Jo Holliday, Lorna M. Gibson, Steve Garratt, Niels Oesingmann, Fidel Alfaro-Almagro, Jimmy D. Bell, Chris Boultwood, Rory Collins, Megan C. Conroy, Nicola Crabtree, Nicola Doherty (+12 others)
2020 Nature Communications  
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

Cecilia A. Callejas Pastor, Il-Young Jung, Shinhye Seo, Soon Bin Kwon, Yunseo Ku, Jayoung Choi
2020 Diagnostics  
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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