Benchmarking 2D human pose estimators and trackers for workflow analysis in the cardiac catheterization laboratory

Abstract

Workflow insights can improve efficiency and safety in the Cardiac Catheterization Laboratory (Cath Lab). As manual analysis is labor-intensive, we aim for automation through camera monitoring. Literature shows that human poses are indicative of activities and therefore workflow. As a first exploration, we evaluate how marker-less multi-human pose estimators perform in the Cath Lab. We annotated poses in 2040 frames from ten multi-view coronary angiogram (CAG) recordings. Pose estimators AlphaPose, OpenPifPaf and OpenPose were run on the footage. Detection and tracking were evaluated separately for the Head, Arms, and Legs with Average Precision (AP), head-guided Percentage of Correct Keypoints (PCKh), Association Accuracy (AA), and Higher-Order Tracking Accuracy (HOTA). We give qualitative examples of results for situations common in the Cath Lab, with reflections in the monitor or occlusion of personnel. AlphaPose performed best on most mean Full-pose metrics with an AP from 0.56 to 0.82, AA from 0.55 to 0.71, and HOTA from 0.58 to 0.73. On PCKh OpenPifPaf scored highest, from 0.53 to 0.64. Arms, Legs, and the Head were detected best in that order, from the views which see the least occlusion. During tracking in the Cath Lab, AlphaPose tended to swap identities and OpenPifPaf merged different individuals. Results suggest that AlphaPose yields the most accurate confidence scores and limbs, and OpenPifPaf more accurate keypoint locations in the Cath Lab. Occlusions and reflection complicate pose tracking. The AP of up to 0.82 suggests that AlphaPose is a suitable pose detector for workflow analysis in the Cath Lab, whereas its HOTA of up to 0.73 here calls for another tracking solution.