UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences
Background Brightness-mode (B-mode) ultrasound is a valuable tool to non-invasively image skeletal muscle architectural changes during movement, but automatically tracking muscle fascicles remains a major challenge. Existing fascicle tracking algorithms either require time-consuming drift correction...
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PeerJ Inc.
2025-01-01
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| author | Tim J. van der Zee Paolo Tecchio Daniel Hahn Brent J. Raiteri |
| author_facet | Tim J. van der Zee Paolo Tecchio Daniel Hahn Brent J. Raiteri |
| author_sort | Tim J. van der Zee |
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| description | Background Brightness-mode (B-mode) ultrasound is a valuable tool to non-invasively image skeletal muscle architectural changes during movement, but automatically tracking muscle fascicles remains a major challenge. Existing fascicle tracking algorithms either require time-consuming drift corrections or yield noisy estimates that require post-processing. We therefore aimed to develop an algorithm that tracks fascicles without drift and with low noise across a range of experimental conditions and image acquisition settings. Methods We applied a Kalman filter to combine fascicle length and fascicle angle estimates from existing and openly-available UltraTrack and TimTrack algorithms into a hybrid algorithm called UltraTimTrack. We applied the hybrid algorithm to ultrasound image sequences collected from the human medial gastrocnemius of healthy individuals (N = 8, four women), who performed cyclical submaximal plantar flexion contractions or remained at rest during passive ankle joint rotations at given frequencies and amplitudes whilst seated in a dynamometer chair. We quantified the algorithm’s tracking accuracy, noise, and drift as the respective mean, cycle-to-cycle variability, and accumulated between-contraction variability in fascicle length and fascicle angle. We expected UltraTimTrack’s estimates to be less noisy than TimTrack’s estimates and to drift less than UltraTrack’s estimates across a range of conditions and image acquisition settings. Results The proposed algorithm yielded low-noise estimates like UltraTrack and was drift-free like TimTrack across the broad range of conditions we tested. Over 120 cyclical contractions, fascicle length and fascicle angle deviations of UltraTimTrack accumulated to 2.1 ± 1.3 mm (mean ± sd) and 0.8 ± 0.7 deg, respectively. This was considerably less than UltraTrack (67.0 ± 59.3 mm, 9.3 ± 8.6 deg) and similar to TimTrack (1.9 ± 2.2 mm, 0.9 ± 1.0 deg). Average cycle-to-cycle variability of UltraTimTrack was 1.4 ± 0.4 mm and 0.6 ± 0.3 deg, which was similar to UltraTrack (1.1 ± 0.3 mm, 0.5 ± 0.1 deg) and less than TimTrack (3.5 ± 1.0 mm, 1.4 ± 0.5 deg). UltraTimTrack was less affected by experimental conditions and image acquisition settings than its parent algorithms. It also yielded similar or lower root-mean-square deviations from manual tracking for previously published image sequences (fascicle length: 2.3–2.6 mm, fascicle angle: 0.8–0.9 deg) compared with a recently-proposed hybrid algorithm (4.7 mm, 0.9 deg), and the recently-proposed DL_Track algorithm (3.8 mm, 3.9 deg). Furthermore, UltraTimTrack’s processing time (0.2 s per image) was at least five times shorter than that of these recently-proposed algorithms. Conclusion We developed a Kalman-filter-based algorithm to improve fascicle tracking from B-mode ultrasound image sequences. The proposed algorithm provides low-noise, drift-free estimates of muscle architectural changes that may better inform muscle function interpretations. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-a05752201d9d4f7f8e33f8624095dd552025-01-26T15:05:10ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e263610.7717/peerj-cs.2636UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequencesTim J. van der Zee0Paolo Tecchio1Daniel Hahn2Brent J. Raiteri3Biomedical Engineering Graduate Program, University of Calgary, Calgary, CanadaDepartment of Human Movement Science, Ruhr University Bochum, Bochum, GermanyDepartment of Human Movement Science, Ruhr University Bochum, Bochum, GermanyDepartment of Human Movement Science, Ruhr University Bochum, Bochum, GermanyBackground Brightness-mode (B-mode) ultrasound is a valuable tool to non-invasively image skeletal muscle architectural changes during movement, but automatically tracking muscle fascicles remains a major challenge. Existing fascicle tracking algorithms either require time-consuming drift corrections or yield noisy estimates that require post-processing. We therefore aimed to develop an algorithm that tracks fascicles without drift and with low noise across a range of experimental conditions and image acquisition settings. Methods We applied a Kalman filter to combine fascicle length and fascicle angle estimates from existing and openly-available UltraTrack and TimTrack algorithms into a hybrid algorithm called UltraTimTrack. We applied the hybrid algorithm to ultrasound image sequences collected from the human medial gastrocnemius of healthy individuals (N = 8, four women), who performed cyclical submaximal plantar flexion contractions or remained at rest during passive ankle joint rotations at given frequencies and amplitudes whilst seated in a dynamometer chair. We quantified the algorithm’s tracking accuracy, noise, and drift as the respective mean, cycle-to-cycle variability, and accumulated between-contraction variability in fascicle length and fascicle angle. We expected UltraTimTrack’s estimates to be less noisy than TimTrack’s estimates and to drift less than UltraTrack’s estimates across a range of conditions and image acquisition settings. Results The proposed algorithm yielded low-noise estimates like UltraTrack and was drift-free like TimTrack across the broad range of conditions we tested. Over 120 cyclical contractions, fascicle length and fascicle angle deviations of UltraTimTrack accumulated to 2.1 ± 1.3 mm (mean ± sd) and 0.8 ± 0.7 deg, respectively. This was considerably less than UltraTrack (67.0 ± 59.3 mm, 9.3 ± 8.6 deg) and similar to TimTrack (1.9 ± 2.2 mm, 0.9 ± 1.0 deg). Average cycle-to-cycle variability of UltraTimTrack was 1.4 ± 0.4 mm and 0.6 ± 0.3 deg, which was similar to UltraTrack (1.1 ± 0.3 mm, 0.5 ± 0.1 deg) and less than TimTrack (3.5 ± 1.0 mm, 1.4 ± 0.5 deg). UltraTimTrack was less affected by experimental conditions and image acquisition settings than its parent algorithms. It also yielded similar or lower root-mean-square deviations from manual tracking for previously published image sequences (fascicle length: 2.3–2.6 mm, fascicle angle: 0.8–0.9 deg) compared with a recently-proposed hybrid algorithm (4.7 mm, 0.9 deg), and the recently-proposed DL_Track algorithm (3.8 mm, 3.9 deg). Furthermore, UltraTimTrack’s processing time (0.2 s per image) was at least five times shorter than that of these recently-proposed algorithms. Conclusion We developed a Kalman-filter-based algorithm to improve fascicle tracking from B-mode ultrasound image sequences. The proposed algorithm provides low-noise, drift-free estimates of muscle architectural changes that may better inform muscle function interpretations.https://peerj.com/articles/cs-2636.pdfMedial gastrocnemiusMuscle architectureFascicle lengthUltrasound imagingAnkle dynamometryB-mode ultrasonography |
| spellingShingle | Tim J. van der Zee Paolo Tecchio Daniel Hahn Brent J. Raiteri UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences PeerJ Computer Science Medial gastrocnemius Muscle architecture Fascicle length Ultrasound imaging Ankle dynamometry B-mode ultrasonography |
| title | UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences |
| title_full | UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences |
| title_fullStr | UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences |
| title_full_unstemmed | UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences |
| title_short | UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences |
| title_sort | ultratimtrack a kalman filter based algorithm to track muscle fascicles in ultrasound image sequences |
| topic | Medial gastrocnemius Muscle architecture Fascicle length Ultrasound imaging Ankle dynamometry B-mode ultrasonography |
| url | https://peerj.com/articles/cs-2636.pdf |
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