TimTrack: A drift-free algorithm for estimating geometric muscle features from ultrasound images

Abstract

Ultrasound imaging is valuable for non-invasively estimating fascicle lengths and other features of pennate muscle, especially when performed computationally. Effective analysis techniques to date typically use optic flow to track displacements from image sequences, but are sensitive to integration drift for longer sequences. We here present an alternative algorithm that objectively estimates geometric features of pennate muscle from ultrasound images, without drift sensitivity. The algorithm identifies aponeuroses and estimates fascicle angles to derive fascicle lengths. Length estimates of human vastus lateralis and lateral gastrocnemius in healthy subjects (N = 9 and N = 1 respectively) compared well (root-mean-square error, RMSE < 0.80 cm) to manual estimates by independent observers (n = 3). The coefficient of multiple correlation (CMC) with manual estimates of fascicle length was comparable to previously reported for state-of-the-art optic flow algorithm (0.93-0.99), suggesting similar accuracy. The algorithm requires minimal manual intervention and can optionally extrapolate fascicle lengths that extend beyond the image frame. It facilitates automated analysis of ultrasound images without drift.