Combined 2D and 3D tracking of surgical instruments for minimally invasive and robotic-assisted surgery

• Published in: International journal for computer assisted radiology and surgery Vol. 11; no. 6; pp. 1109 - 1119
• Main Authors: Du, Xiaofei, Allan, Maximilian, Dore, Alessio, Ourselin, Sebastien, Hawkes, David, Kelly, John D., Stoyanov, Danail
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2016
• Subjects: Computer Imaging; Computer Science; Health Informatics; Humans; Imaging; Medicine; Medicine & Public Health; Minimally Invasive Surgical Procedures - instrumentation; Minimally Invasive Surgical Procedures - methods; Motion; Original; Original Article; Pattern Recognition and Graphics; Radiology; Robotic Surgical Procedures - instrumentation; Robotic Surgical Procedures - methods; Surgery; Surgery, Computer-Assisted - instrumentation; Surgery, Computer-Assisted - methods; Surgical Instruments; Vision; Instrument tracking and detection; Surgical vision; Minimally invasive surgery; Robot-assisted surgery
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-016-1393-4

Purpose Computer-assisted interventions for enhanced minimally invasive surgery (MIS) require tracking of the surgical instruments. Instrument tracking is a challenging problem in both conventional and robotic-assisted MIS, but vision-based approaches are a promising solution with minimal hardware integration requirements. However, vision-based methods suffer from drift, and in the case of occlusions, shadows and fast motion, they can be subject to complete tracking failure. Methods In this paper, we develop a 2D tracker based on a Generalized Hough Transform using SIFT features which can both handle complex environmental changes and recover from tracking failure. We use this to initialize a 3D tracker at each frame which enables us to recover 3D instrument pose over long sequences and even during occlusions. Results We quantitatively validate our method in 2D and 3D with ex vivo data collected from a DVRK controller as well as providing qualitative validation on robotic-assisted in vivo data. Conclusions We demonstrate from our extended sequences that our method provides drift-free robust and accurate tracking. Our occlusion-based sequences additionally demonstrate that our method can recover from occlusion-based failure. In both cases, we show an improvement over using 3D tracking alone suggesting that combining 2D and 3D tracking is a promising solution to challenges in surgical instrument tracking.