Abstract. In the context of retinal microsurgery, visual tracking of instruments is a key component of robotics assistance. The difficulty of the task and major reason why most existing strategies fail on in-vivo image sequences lies in the fact that complex and severe changes in instrument appearance are challenging to model. This paper introduces a novel approach, that is both data-driven and complementary to existing tracking techniques. In particular, we show how to learn and integrate an accurate detector with a simple gradient-based tracker within a robust pipeline which runs at framerate. In addition, we present a fully annotated dataset of retinal instruments in in-vivo surgeries, which we use to quantitatively validate our approach. We also demonstrate an application of our method in a laparascopy image sequence.
Methods for tracking an object have generally fallen into two groups: tracking by detection and tracking through local optimization. The advantage of detection-based tracking is its ability to deal with target appearance and disappearance, but it does not naturally take advantage of target motion continuity during detection. The advantage of local optimization is efficiency and accuracy, but it requires additional algorithms to initialize tracking when the target is lost. To bridge these two approaches, we propose a framework for unified detection and tracking as a time-series Bayesian estimation problem. The basis of our approach is to treat both detection and tracking as a sequential entropy minimization problem, where the goal is to determine the parameters describing a target in each frame. To do this we integrate the Active Testing (AT) paradigm with Bayesian filtering, and this results in a framework capable of both detecting and tracking robustly in situations where the target object enters and leaves the field of view regularly. We demonstrate our approach on a retinal tool tracking problem and show through extensive experiments that our method provides an efficient and robust tracking solution.
Minimally invasive cardiac surgery offers important benefits for the patient but it also imposes several challenges for the surgeon. Robotic assistance has been proposed to overcome many of the difficulties inherent to the minimally invasive procedure, but so far no solutions for compensating physiological motion are present in the existing surgical robotic platforms. In beating heart surgery, cardiac and respiratory motions are important sources of disturbance, hindering the surgeon’s gestures and limiting the types of procedures that can be performed in a minimally invasive fashion. In this context, computer vision techniques can be used for retrieving the heart motion for active motion stabilization, which improves the precision and repeatability of the surgical gestures. However, efficient tracking of the heart surface is a challenging problem due to the heart surface characteristics, large deformations and the complex illumination conditions. In this article, we present an efficient method for active cancellation of cardiac motion where we combine an efficient algorithm for 3D tracking of the heart surface based on a thin-plate spline deformable model and an illumination compensation algorithm able to cope with arbitrary illumination changes. The proposed method has two novelties: the thin-plate spline model for representing the heart surface deformations and an efficient parametrization for 3D tracking of the beating heart using stereo images from a calibrated stereo endoscope. The proposed tracking method has been evaluated offline on in vivo images acquired by a DaVinci surgical robotic platform.
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