#5569. Automatic tracking of healthy joint kinematics from stereo-radiography sequences

July 2026publication date
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Journal’s subject area:
Health Informatics;
Computer Science Applications;
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Abstract:
Kinematic tracking of healthy joints in radiography sequences is frequently performed by maximizing similarities between computed perspective projections of 3D computer models and corresponding objects’ appearances in radiographic images. Significant human effort associated with manual tracking presents a major bottleneck in biomechanics research methods and limits the scale of target applications. The current work introduces a method for fully-automatic tracking of tibiofemoral and patellofemoral kinematics in stereo-radiography sequences for subjects performing dynamic activities. The proposed method involves the application of convolutional neural networks for annotating radiographs and a multi-stage optimization pipeline for estimating bone pose based on information provided by neural net predictions. Predicted kinematics are evaluated by comparing against manually-tracked trends across 20 distinct trials.
Keywords:
Biomechanics; Deep learning; Knee; Medical imaging; Optimization

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