MSc ME Thesis presentation

Detecting Medical Equipment in the Catheterization Laboratory using Computer Vision

Renjie Dai

Workflow analysis aims to improve the efficiency and safety in operating rooms by analyzing surgical processes and providing feedback or support, where observations can be made and evaluated by algorithms rather than human experts. For our study, we mount five calibrated cameras from different angles in a Catheterization Laboratory (Cath Lab) to observe and analyze Cardiac Angiogram procedures. To automate the classification of workflow and personnel activities, we propose an object detection algorithm based on Scaled-YOLOv4 with a filter to improve bounding box prediction. Scaled-YOLOv4, as a state-of-the-art technique, is featured with extremely fast processing speed and decent precision. However, we find that Scaled-YOLOv4 still suffers when detecting objects with flexible appearance due to fixed anchors. This can result in the prediction of bounding boxes missing parts of the object or containing a blank environment. In this work, we design a filter following Scaled-YOLOv4 to improve the prediction of the bounding box by matching the features detected from different cameras. With the keypoints detected by SuperPoint and matched by SuperGLue, the filter adjusts the boundaries of the bounding box to include all the matched keypoints. The proposed algorithm achieves 95.1% mAP in detecting medical equipment in the Catheterization Laboratory and a real-time speed of 58 FPS on RTX 3090.