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X-ray Imaging

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...

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Related Experiment Video

Updated: Sep 25, 2025

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Published on: March 12, 2021

Validating markerless pose estimation with 3D X-ray radiography.

Dalton D Moore¹, Jeffrey D Walker², Jason N MacLean^1,3,4

¹Committee on Computational Neuroscience, University of Chicago, Chicago, IL 60637, USA.

The Journal of Experimental Biology

|April 25, 2022

Summary

This summary is machine-generated.

DeepLabCut (DLC), a markerless motion capture method, accurately tracks primate movement. This validated approach allows detailed study of natural behaviors, crucial for understanding neurophysiology.

Keywords:

Anipose DeepLabCut Markerless tracking Marmoset Pose estimation XROMM

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Last Updated: Sep 25, 2025

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Published on: July 2, 2021

Area of Science:

Neuroscience
Biomechanics
Computer Vision

Background:

Accurate tracking of limb and posture is essential for understanding the neurophysiological basis of natural movement.
Markerless motion capture offers a promising avenue for studying complex behaviors without invasive markers.

Purpose of the Study:

To evaluate the accuracy of DeepLabCut (DLC), a deep learning-based markerless motion capture system.
To compare DLC's accuracy against a gold-standard 3D X-ray videoradiography (XROMM) system for tracking primate forelimb and torso kinematics.

Main Methods:

Simultaneous recording of behavioral data using XROMM and RGB video of foraging marmosets.
Utilizing the Anipose toolkit to filter and triangulate DLC-derived marker trajectories.
Reconstructing 3D kinematics in a common coordinate system for comparison.

Main Results:

DLC demonstrated a low median error of 0.228 cm when compared to XROMM.
This error represented only 2.0% of the total range of motion for the tracked markers.
High accuracy was achieved for marker trajectories on the marmoset forelimb and torso.

Conclusions:

DeepLabCut provides a highly accurate markerless motion capture solution for studying primate motor control.
The validated accuracy of DLC enables the investigation of naturalistic behaviors in various research fields.
Markerless systems like DLC are valuable tools for advancing the study of neurophysiology and behavior.