Probabilistic Tracking of Affine-Invariant Anisotropic Regions

Abstract

Abstract-Despite a wide range of feature detectors developed in the computer vision community over the years, direct application of these techniques to surgical navigation has shown significant difficulties due to the paucity of reliable salient features coupled with free-form tissue deformation and changing visual appearance of surgical scenes. The aim of this paper is to propose a novel probabilistic framework to track affine-invariant anisotropic regions under contrastingly different visual appearances during Minimally Invasive Surgery (MIS). The theoretical background of the affine-invariant anisotropic feature detector is presented and a real-time implementation exploiting the computational power of the GPU is proposed. An Extended Kalman Filter (EKF) parameterisation scheme is used to adaptively adjust the optimal templates of the detected regions, enabling accurate identification and matching of the tracked features. For effective tracking verification, spatial context and region similarity have also been incorporated. They are used to boost the prediction of the EKF and recover potential tracking failure due to drift or false positives. The proposed framework is compared to the existing methods and their respective performance is evaluated with in vivo video sequences recorded from robotic assisted MIS procedures, as well as real-world scenes.

Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery

“…The performances of Method 1 and Method 2 are evaluated against the metrics of accuracy, computational time, and percentage of matched pixels. The Hamlyn Centre Laparoscopic/Endoscopic Video dataset [7], with an associated ground truth representing a surgical scene, is used for the evaluation.…”

Online tracking and retargeting with applications to optical biopsy in gastrointestinal endoscopic examinations

“…In surgical vision, detection and tracking have been topics of particular interest, which include both surgical instrument tracking or detection (Sznitman et al, 2012(Sznitman et al, , 2013(Sznitman et al, , 2014Reiter et al, 2014), and tissue tracking (Mountney et al, 2007;Mountney and Yang, 2008;Richa et al, 2011Richa et al, , 2012Giannarou et al, 2013). In this paper, we propose an Online Tracking and Retargeting (OTR) framework for optical biopsy.…”

EnViSoRS: Enhanced Vision System for Robotic Surgery. A User-Defined Safety Volume Tracking to Minimize the Risk of Intraoperative Bleeding

“…In order to overcome these limitations and to update the preoperative/intraoperative registration at run-time, computer vision and image-based tracking algorithms can be exploited to track these areas relying on image characteristics (Stoyanov, 2012). Different attempts have been made in literature for soft tissue tracking in endoscopic images (Yip et al, 2012;Giannarou et al, 2013;Puerto-Souza et al, 2014;Richa et al, 2014). Furthermore, to track the region of interest in the 3D space and to have a more accurate knowledge of the tissue deformation, 3D reconstruction algorithms are exploited to reconstruct the surface of the surgical area (Stoyanov et al, 2010;Röhl et al, 2012;Maier-Hein et al, 2013.…”