Reliability and accuracy of an automated tracking algorithm to measure controlled passive and active muscle fascicle length changes from ultrasound

Abstract

Manual tracking of muscle fascicle length changes from ultrasound images is a subjective and time-consuming process. The purpose of this study was to assess the repeatability and accuracy of an automated algorithm for tracking fascicle length changes in the medial gastrocnemius (MG) muscle during passive length changes and active contractions (isometric, concentric and eccentric) performed on a dynamometer. The freely available, automated tracking algorithm was based on the Lucas–Kanade optical flow algorithm with an affine optic flow extension, which accounts for image translation, dilation, rotation and shear between consecutive frames of an image sequence. Automated tracking was performed by three experienced assessors, and within- and between-examiner repeatability was computed using the coefficient of multiple determination (CMD). Fascicle tracking data were also compared with manual digitisation of the same image sequences, and the level of agreement between the two methods was calculated using the coefficient of multiple correlation (CMC). The CMDs across all test conditions ranged from 0.50 to 0.93 and were all above 0.98 when recomputed after the systematic error due to the estimate of the initial fascicle length on the first ultrasound frame was removed from the individual fascicle length waveforms. The automated and manual tracking approaches produced similar fascicle length waveforms, with an overall CMC of 0.88, which improved to 0.94 when the initial length offset was removed. Overall results indicate that the automated fascicle tracking algorithm was a repeatable, accurate and time-efficient method for estimating fascicle length changes of the MG muscle in controlled passive and active conditions.

Keywords::

• ultrasonography
• medial gastrocnemius
• affine flow
• muscle fascicle tracking

Acknowledgements

This work was supported by funding from the Griffith University Australian Postgraduate Award and the International Society of Biomechanics Congress Travel Grant. The authors acknowledge the contribution of Dr David Young (University of Sussex) for developing the Matlab code for the affine optic flow model which was used in this research (http://www.mathworks.com/matlabcentral/fileexchange/27093-affine-optic-flow) and also for correspondence regarding its implementation.

Conflict of interest statement: No financial or personal relationships were conducted with other people or organisations that could inappropriately influence or bias this work.

Notes

^1. Email: [email protected]

^2. Email: [email protected]