OpenArm 2.0: Automated Segmentation of 3D Tissue Structures for Multi-Subject Study of Muscle Deformation Dynamics.

Clicks: 259
ID: 96573
2019
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Abstract
We present a novel neural-network-based pipeline for segmentation of 3D muscle and bone structures from localized 2D ultrasound data of the human arm. Building from the U-Net [1] neural network framework, we examine various data augmentation techniques and training data sets to both optimize the network's performance on our data set and hypothesize strategies to better select training data, minimizing manual annotation time while maximizing performance. We then employ this pipeline to generate the OpenArm 2.0 data set, the first factorial set of multi-subject, multi-angle, multi-force scans of the arm with full volumetric annotation of the biceps and humerus. This data set has been made available on SimTK (https://simtk.org/projects/openarm) to enable future exploration of muscle force modeling, improved musculoskeletal graphics, and assistive device control.
Reference Key
nozik2019openarmconference Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nozik, Yonatan;Hallock, Laura A;Ho, Daniel;Mandava, Sai;Mitchell, Chris;Li, Thomas Hui;Bajcsy, Ruzena;
Journal conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference
Year 2019
DOI
10.1109/EMBC.2019.8857669
URL
Keywords
nuclear medicine Children dynamic contrast-enhanced magnetic resonance imaging renal function glomerular filtration rate kidneys 99mtc diethylenetriamine pentaacetic acid serum creatinine

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