deepPatella. A tool for automatic tracking of Patella Tendon Elongation and stiffness calculation
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Autor:in (Körperschaft)
Publikationsdatum
2026
Typ der Arbeit
Studiengang
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Current Issues in Sport Science
Themenheft
DOI der Originalpublikation
Link
Zugehörige Forschungsdaten
Reihe / Serie
Reihennummer
Jahrgang / Band
11
Ausgabe / Nummer
2
Seiten / Dauer
022
Patentnummer
Verlag / Herausgebende Institution
Bern Open Publishing
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Patellar tendon (PT) stiffness is highly load-sensitive and can decrease by up to 29% after 14 days of unloading, impacting force transfer and thereby rehabilitation processes and athletic performance. While automated ultrasound (US) analysis has been applied to the Achilles tendon, PT elongation—an indirect marker of stiffness—still relies on manual and time-consuming frame-by-frame annotation. We present deepPatella, a novel, automated, transformer-based, open-source software with Kalman (KF) filtering to efficiently (and objectively) track PT elongation from US frame sequences. PT elongation was assessed using US (ArtUS, Telemed) with a 6 cm linear probe (LF9-5N60-A3 @7 MHz) fixed over the PT apex and tibial tuberosity during ramped isometric knee extensions. US images were recorded at 50 fps. Elongation was defined as relative displacement of proximal and distal insertions, manually labelled by two raters. The dataset included 17.475 frames from 30 participants (13 males, age=46.5 years (35-56), weight=73.7 kg (53.1-95.1), height=171.3 cm (158.0-188.0)), split into 12.221/5.254 frames (or 27/14 videos) for training/testing. A TransUNet model with sigmoid-based heatmaps, BCE-Dice loss, and ADAM optimizer predicted insertion points; predictions were subsequently smoothed using a Kalman filter. Accuracy was evaluated via Euclidean distance to manual labels and by comparing predicted and Kalman filtered PT elongation against the manually labelled one. In the 14 test videos, PT elongation was 4.8±1.5 mm based on manual annotation, 4.6±2.8 mm from the model prediction, and 4.6 ±2.9 mm with KF. Mean Euclidean errors compared to manual labels were 0.9±0.5 mm for the distal and 1.0±0.4 mm for the proximal insertions, for both model and Kalman. Accordingly, total absolute elongation Euclidean errors were 1.0±0.9 mm for the model and 1.0±1.8 mm for Kalman. Overall, the model predicted PT insertions with high accuracy and KF smoothed but did not improve the predictions. Additionally, using deepPatella, the processing time of per test video was reduced by 90% from 30 minutes (manual labelling) to 3 minutes. Thus, deepPatella enables time-efficient and objective PT elongation analysis in US videos and provides the basis for automated stiffness estimation. The openly available deepPatella software further enables users to compute tendon elongation and stiffness on their own data.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
2414-6641
Sprache
Andere
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
peer-reviewed
Open Access-Status
Closed
Lizenz
Zitation
Romeny Kunstmann, G., Ledergerber, R., Keller, M., Behr, D., Benini, L., Faude, O., Leitner, C., & Ritsche, P. (2026). deepPatella. A tool for automatic tracking of Patella Tendon Elongation and stiffness calculation. Current Issues in Sport Science, 11(2), 22. https://doi.org/10.36950/2026.2ciss022