Body Fat Estimation from Surface Meshes using Graph Neural Networks

• URL: http://arxiv.org/abs/2308.02493v3
• Date: Tue, 31 Oct 2023 09:04:24 GMT
• Title: Body Fat Estimation from Surface Meshes using Graph Neural Networks
• Authors: Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios Kaissis, Daniel Rueckert
• Abstract summary: We show that triangulated body surface meshes can be used to accurately predict VAT and ASAT volumes using graph neural networks. Our methods achieve high performance while reducing training time and required resources compared to state-of-the-art convolutional neural networks in this area.
• Score: 48.85291874087541
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Frequently used measures for fat estimation are the body mass index (BMI), waist circumference, or the waist-hip-ratio. However, those are rather imprecise measures that do not allow for a discrimination between different types of fat or between fat and muscle tissue. The estimation of visceral (VAT) and abdominal subcutaneous (ASAT) adipose tissue volume has shown to be a more accurate measure for named risk factors. In this work, we show that triangulated body surface meshes can be used to accurately predict VAT and ASAT volumes using graph neural networks. Our methods achieve high performance while reducing training time and required resources compared to state-of-the-art convolutional neural networks in this area. We furthermore envision this method to be applicable to cheaper and easily accessible medical surface scans instead of expensive medical images.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• BMI Prediction from Handwritten English Characters Using a Convolutional Neural Network [0.0]
  No previous research established a clear connection between deep learning techniques for handwriting analysis and BMI prediction. This article addresses this research gap with a deep learning approach to estimating BMI from handwritten characters by developing a convolutional neural network (CNN) A dataset containing samples from 48 people in lowercase English scripts is successfully captured for the BMI prediction task.
  arXiv  Detail & Related papers  (2024-09-04T10:06:42Z)
• Liver Fat Quantification Network with Body Shape [4.642852520437342]
  We propose a deep neural network to estimate the percentage of liver fat using only body shapes. The proposed is composed of a flexible baseline network and a lightweight Attention module. The results verify that our proposed method yields state-of-the-art performance with Root mean squared error (RMSE) of 5.26% and R-Squared value over 0.8.
  arXiv  Detail & Related papers  (2024-05-18T20:22:22Z)
• On the Localization of Ultrasound Image Slices within Point Distribution Models [84.27083443424408]
  Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US) Longitudinal tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. We present a framework for automated US image slice localization within a 3D shape representation.
  arXiv  Detail & Related papers  (2023-09-01T10:10:46Z)
• Machine Learning and Bioinformatics for Diagnosis Analysis of Obesity Spectrum Disorders [0.0]
  The number of obese patients has doubled due to sedentary lifestyles and improper dieting. Life expectancy dropped from 80 to 75 years, as obese people struggle with different chronic diseases. This report will address the problems of obesity in children and adults using ML datasets to feature, predict, and analyze the causes of obesity.
  arXiv  Detail & Related papers  (2022-08-05T13:07:27Z)
• Body Composition Estimation Based on Multimodal Multi-task Deep Neural Network [0.0]
  Body composition is largely made up of muscle, fat, bones, and water. We introduce a multimodal multi-task deep neural network to estimate body fat percentage and skeletal muscle mass.
  arXiv  Detail & Related papers  (2022-05-23T04:31:06Z)
• Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine [72.85011943179894]
  We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
  arXiv  Detail & Related papers  (2022-05-16T10:59:55Z)
• A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography [0.9786690381850356]
  We propose a unified method for an autonomous segmentation and quantification of two types of cardiac fats. The segmented fats are termed epicardial and mediastinal, and stand apart from each other by the pericardium. The proposed methodology mainly comprises registration and classification algorithms to perform the desired segmentation.
  arXiv  Detail & Related papers  (2021-12-21T17:38:06Z)
• Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation [50.63623720394348]
  We propose a loss reweighting approach to increase the ability of the network to detect small lesions. We report the benefit from our method for well-known loss functions, including Dice Loss, Focal Loss, and Asymmetric Similarity Loss. Our experiments show that inverse weighting considerably increases the detection quality, while preserves the delineation quality on a state-of-the-art level.
  arXiv  Detail & Related papers  (2020-07-20T12:08:22Z)
• Robustness to Spurious Correlations via Human Annotations [100.63051542531171]
  We present a framework for making models robust to spurious correlations by leveraging humans' common sense knowledge of causality. Specifically, we use human annotation to augment each training example with a potential unmeasured variable. We then introduce a new distributionally robust optimization objective over unmeasured variables (UV-DRO) to control the worst-case loss over possible test-time shifts.
  arXiv  Detail & Related papers  (2020-07-13T20:05:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.