An enhanced marker pattern that achieves improved accuracy in surgical tool tracking

ABSTRACT

In computer assisted interventions (CAI), surgical tool tracking is crucial for applications such as surgical navigation, surgical skill assessment, visual servoing, and augmented reality. Tracking of cylindrical surgical tools can be achieved by printing and attaching a marker to their shaft. However, the tracking error of existing cylindrical markers is still in the millimetre range, which is too large for applications such as neurosurgery requiring sub-millimetre accuracy. To achieve tool tracking with sub-millimetre accuracy, we designed an enhanced marker pattern, which is captured on images from a monocular laparoscopic camera. The images are used as input for a tracking method which is described in this paper. Our tracking method was compared to the state-of-the-art, on simulation and ex vivo experiments. This comparison shows that our method outperforms the current state-of-the-art. Our marker achieves a mean absolute error of 0.28 [mm] and 0.45 [^°] on ex vivo data, and 0.47 [mm] and 1.46 [^°] on simulation. Our tracking method is real-time and runs at 55 frames per second for $720\times 576$ image resolution.

KEYWORDS:

• Surgical tool tracking
• pose estimation
• cylindrical marker

Acknowledgments

The authors are grateful for the support from the NIHR Imperial BRC (Biomedical Research Centre), the Cancer Research Uk Imperial Centre, the Royal Society (UF140290) and technical support in the form of tool model CAD data from Intuitive Surgical.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

João Cartucho

João Cartucho is a PhD student in the Hamlyn Centre for Robotic Surgery at Imperial College London, UK. His research focus is in Computer Vision, and Robotic Surgery.

Chiyu Wang

Chiyu Wang is an MRes student in the Hamlyn Centre for Robotic Surgery at Imperial College London, UK. His research focus is in Robotic Surgery.

Baoru Huang

Baoru Huang is a PhD student in the Hamlyn Centre for Robotic Surgery at Imperial College London, UK. Her major research focus is in Computer Vision, Deep Learning and Surgical Augmented Reality.

Dan S. Elson

Dan S. Elson is a Professor of Surgical Imaging and Biophotonics in the Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation and Department of Surgery and Cancer, UK. Research interests are based around the development and application of photonics technologies to medical imaging and endoscopy.

Ara Darzi

Ara Darzi is the Paul Hamlyn Chair of Surgery at Imperial College London, the Royal Marsden Hospital and the Institute of Cancer Research. He is Director of the Institute of Global Health Innovation at Imperial College London and Chair of Imperial College Health Partners. He is an Honorary Consultant Surgeon at Imperial College Hospital NHS Trust.

Stamatia Giannarou

Stamatia Giannarou is a Royal Society University Research Fellow at the Hamlyn Centre for Robotic Surgery, Imperial College London, UK. Her main research interests include visual recognition and surgical vision.