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Stochastic Analysis And Modelling
An Accuracy Assessment Of Slicerautoscoperm - Software For Tracking Skeletal Structures In Multi-plane Videoradiography Datasets.

Home
Research Domains
Mathematical Sciences
Statistics
Stochastic Analysis And Modelling
An Accuracy Assessment Of Slicerautoscoperm - Software For Tracking Skeletal Structures In Multi-plane Videoradiography Datasets.

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In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

An accuracy assessment of SlicerAutoscoper^M - software for tracking skeletal structures in multi-plane videoradiography datasets.

Amy M Morton¹, John D Holtgrewe¹, Jillian E Beveridge¹

¹Bioengineering Laboratory, Department of Orthopaedics, The Warren Alpert Medical School of Brown University and Brown University Health, Providence RI 02903, United States.

Journal of Biomechanics

|June 14, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

SlicerAutoscoper (SAM) enhances markerless tracking for skeletal joint kinematics using biplane videoradiography. This validated tool ensures accurate in vivo motion analysis, promoting research collaboration and clinical translation.

Keywords:

3DSlicer Autoscoper Kinematics Skeletal Tracking

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Area of Science:

Biomechanical Engineering
Medical Imaging Analysis
Musculoskeletal Research

Background:

Biplane videoradiography (BVR) is crucial for in vivo skeletal joint kinematics.
Markerless tracking software requires standardization for broader research application.

Purpose of the Study:

Introduce SlicerAutoscoper (SAM), an upgraded markerless tracking software.
Establish a standardized workflow for musculoskeletal research using 3DSlicer.
Evaluate the accuracy and agreement of SAM for tracking multiple skeletal joints.

Main Methods:

Integrated Autoscoper software into 3DSlicer with a pre-processing module.
Assessed accuracy by comparing SAM-derived kinematics with marker-based data.
Quantified bias and limits of agreement using Bland-Altman analysis for foot, knee, shoulder, and wrist joints.

Videoradiography

Main Results:

SAM demonstrated robust tracking accuracy across all four skeletal joints.
Mean errors for the foot were <0.5° (rotational) and <0.8 mm (translational).
Knee, shoulder, and wrist joint tracking showed high accuracy with minimal translational and rotational errors.

Conclusions:

SlicerAutoscoper (SAM) is a validated, accurate tool for image-based skeletal motion tracking.
SAM facilitates collaboration, method harmonization, and lowers the barrier for researchers.
The software supports translation towards clinical applications in musculoskeletal research.