Abdi, Abas, Jeviskov, Jevgeni, Hussain, Arshian, Alibakshi, Alireza, Ufumaka, Isreal, Alajrami, Eman, Francis, Darrel P., Serej, Nasim Dadashi and Zolgharni, Massoud ORCID: https://orcid.org/0000-0003-0904-2904 (2026) Regional myocardial strain estimation with MyoTrackerChrono. In: Artificial Intelligence in Healthcare. Lecture Notes in Computer Science, 16877. Springer, pp. 131-142.

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Abstract

Cardiovascular diseases require precise, non-invasive diagnostic tools to assess regional myocardial function. While Speckle Tracking Echocardiography (STE) is widely used to measure myocardial strain, traditional block-matching and optical flow algorithms frequently fail due to acoustic noise and out-of-plane speckle decorrelation. This paper introduces MyoTrackerChrono, a hybrid deep learning architecture designed for robust, sparse point tracking in echocardiography. By integrating a Temporal Adapter between a spatial convolutional encoder and a tracking transformer, the model explicitly aggregates temporal context early in the feature extraction phase. This allows for stable tracking even during rapid cardiac phases or momentary signal loss. Validated on physics-based synthetic benchmarks, MyoTrackerChrono improves pixel-level tracking accuracy (Mean Absolute Error) by 33.0% over baseline models and reduces strain estimation error in mechanically complex mid-wall segments by over 64%. Furthermore, the architecture operates at an effective inference rate of 1382 FPS, confirming its feasibility for rapid post-acquisition clinical deployment

Item Type:	Book Chapter or Section
Identifier:	10.1007/978-3-032-35393-1_10
Additional Information:	Book chapter of a conference paper presented in the International Conference on AI in Healthcare (AIiH), 28-28 August, London, United Kingdom.
Subjects:	Computing > Intelligent systems Medicine and health
Related URLs:	Organisation
Date Deposited:	24 Sep 2026
Dates:	Date Publication status 22 May 2026 Accepted 12 August 2026 Published Online
School, department or research centre:	School of Computing and Engineering THRIVE (The Centre for Translational Healthcare Research, Innovation, Vision, and Excellence)
URI:	https://repository.uwl.ac.uk/id/eprint/15316

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