Towards real-time multiple surgical tool tracking. Issue 3 (4th May 2021)

• Record Type: Journal Article
• Authors: Robu, Maria; Kadkhodamohammadi, Abdolrahim; Luengo, Imanol; Stoyanov, Danail
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 9:Issue 3(2021)
• Page Start: 279
• Page End: 285
• Publication Date: 2021-05-04
• Subjects: Multiple tool tracking -- Surgical data science -- Computer-Assisted Interventions; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; 616.0757
• DOI: 10.1080/21681163.2020.1835553
• Languages: English
• ISSNs: 2168-1163

Abstract

ABSTRACT: Surgical tool tracking is an essential building block for computer-assisted interventions (CAI) and applications like video summarisation, workflow analysis and surgical navigation. Vision-based instrument tracking in laparoscopic surgical data faces significant challenges such as fast instrument motion, multiple simultaneous instruments and re-initialisation due to out-of-view conditions or instrument occlusions. In this paper, we propose a real-time multiple object tracking framework for whole laparoscopic tools, which extends an existing single object tracker. We introduce a geometric object descriptor, which helps with overlapping bounding box disambiguation, fast motion and optimal assignment between existing trajectories and new hypotheses. We achieve 99.51% and 75.64% average accuracy on ex-vivo robotic data and in-vivo laparoscopic sequences respectively from the Endovis'15 Instrument Tracking Dataset. The proposed geometric descriptor increased the performance on laparoscopic data by 32%, significantly reducing identity switches, false negatives and false positives. Overall, the proposed pipeline can successfully recover trajectories over long-sequences and it runs in real-time at approximately 25–29 fps.