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2D human pose tracking in the cardiac catheterisation laboratory with BYTE
Rick M Butler1, Teddy S Vijfvinkel1, Emanuele Frassini1
1Delft University of Technology, Delft, the Netherlands.
Insights
This study introduces a new human pose tracker for Cardiac Catheterisation Laboratories (Cath Labs). The tracker improves workflow analysis by accurately re-identifying individuals without visual data, enhancing safety and efficiency.
Area of Science:
- Medical Imaging and Computer Vision
- Healthcare Workflow Optimization
Background:
- Cardiac Catheterisation Laboratories (Cath Labs) generate valuable data for workflow analysis.
- Human pose tracking from video offers insights into laboratory efficiency and safety.
- Challenges in Cath Labs include occlusions and visual similarity of personnel, hindering accurate re-identification.
Purpose of the Study:
- To develop and evaluate a novel human pose tracker for Cardiac Catheterisation Laboratories.
- To address challenges of occlusion and personnel re-identification in Cath Lab environments.
- To assess the tracker's performance across different surgical phases and compare it with state-of-the-art methods.
Main Methods:
- A human pose tracker was developed using object keypoint similarity and a third-order motion model, excluding visual re-identification.
- The algorithm was tested on real coronary angiogram recordings from a Cath Lab.
- Performance was measured using Higher-Order Tracking Accuracy (HOTA) across five distinct surgical steps.
Main Results:
- The proposed tracker achieved up to 0.71 HOTA, outperforming state-of-the-art trackers (up to 0.65 HOTA).
- The tracker demonstrated more consistent performance across workflow phases (10 pp variation) compared to others (up to 23 pp).
- Achieved processing speeds of 22.5 frames per second, 9 fps faster than current methods.
Conclusions:
- The novel pose tracker is effective for Cath Lab workflow analysis, offering improved accuracy and consistency.
- Its speed and stability support real-time applications in healthcare settings.
- The publicly available code facilitates further research and development in this area.
Abstract:
Workflow insights can enable safety- and efficiency improvements in the Cardiac Catheterisation Laboratory (Cath Lab). Human pose tracklets from video footage can provide a source of workflow information. However, occlusions and visual similarity between personnel make the Cath Lab a challenging environment for the re-identification of individuals. We propose a human pose tracker that addresses these problems specifically, and test it on recordings of real coronary angiograms. This tracker uses no visual information for re-identification, and instead employs object keypoint similarity between detections and predictions from a third-order motion model. Algorithm performance is measured on Cath Lab footage using Higher-Order Tracking Accuracy (HOTA). To evaluate its stability during procedures, this is done separately for five different surgical steps of the procedure. We achieve up to 0.71 HOTA where tested state-of-the-art pose trackers score up to 0.65 on the used dataset. We observe that the pose tracker HOTA performance varies with up to 10 percentage point (pp) between workflow phases, where tested state-of-the-art trackers show differences of up to 23 pp. In addition, the tracker achieves up to 22.5 frames per second, which is 9 frames per second faster than the current state-of-the-art on our setup in the Cath Lab. The fast and consistent short-term performance of the provided algorithm makes it suitable for use in workflow analysis in the Cath Lab and opens the door to real-time use-cases. Our code is publicly available at https://github.com/RM-8vt13r/PoseBYTE.
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