Context specific descriptors for tracking deforming tissue

Abstract

Abstract.In minimally invasive surgery, deployment of motion compensation, dynamic active constraints and adaptive intra-operative guidance require accurate estimation of deforming tissue in 3D. To this end, the use of vision-based techniques is advantageous in that it does not require the integration of additional hardware to the existing surgical settings.Deformation can be recovered by tracking features on the surface of the tissue. Existing methods are mostly based on ad hoc machine vision techniques that have generally been developed for rigid scenes or use pre-determined models with parameters fine tuned to specific surgical settings. In this work, we propose a novel tracking technique based on a context specific feature descriptor. The descriptor can adapt to its surroundings and identify the most discriminate information for tracking. The feature descriptor is represented as a decision tree and the tracking process is formulated as a classification problem for which log likelihood ratios are used to improve classifier training. A recursive tracking algorithm is used to obtain examples of tissue deformation used to train the classifier. Additional training data is generated by geometric and appearance modeling. Experimental results have shown that the proposed context specific descriptor is robust to drift, occlusion, and changes in orientation and scale. The performance of the algorithm is compared with existing tracking algorithms and validated with both simulated and in vivo datasets.

Visual servoing in medical robotics: a survey. Part II: tomographic imaging modalities - techniques and applications

“…For example, Mountney et al . proposed a context‐specific feature descriptor that can adapt to changes in the environment. These methods can potentially be used for visual servoing if they are computationally efficient and provide results that are robust and accurate.…”

Stitching and Surface Reconstruction From Endoscopic Image Sequences: A Review of Applications and Methods

“…This is true for all stitching and reconstruction methods that include a global optimization step, such as bundle adjustment. The SLAM methods, primarily applied in laparoscopy, are an exception to this -as presented by Mountney and Yang's group [104]- [110] as a stereoscopic approach, as well as by Grasa et al [58]- [60] for monoscopic views (see section II-D). Bouma et al [26] have also presented a realtime reconstruction approach for minimally invasive surgery, which incorporates stereoscopic ego-motion computation in real-time.…”

“…To the best of our knowledge, none of these has yet been applied to endoscopic stitching, despite their high potential. An exception is the work of Mountney et al [107], [110] (2008, 2011), who have presented an online learning scheme for feature descriptors and adapted the method of randomized trees for keypoint recognition by Lepetit et al [86] (2006) to develop contextspecific descriptors for application in laparoscopic stereoscopy SLAM. They have also presented a comparison of feature descriptors for MIS [104] (2007).…”

Content-based processing and analysis of endoscopic images and videos: A survey

“…A comparative evaluation of state-of-the-art feature-matching algorithms for endoscopic images has been carried out in [186]. [155,187,188,205,231,268], deforming tissue tracking is a very hard research challenge that still requires a lot of further work. Endoscopic videos feature many domain-induced problems like scarcity of distinctive landmarks because of homogenous surfaces and indistinctive texture that makes it hard to find good points to track.…”