Vision-Based Proximity Detection in Retinal Surgery

Abstract

In retinal surgery, surgeons face difficulties such as indirect visualization of surgical targets, physiological tremor and lack of tactile feedback, which increase the risk of retinal damage caused by incorrect surgical gestures. In this context, intra-ocular proximity sensing has the potential to overcome current technical limitations and increase surgical safety. In this paper we present a system for detecting unintentional collisions between surgical tools and the retina using the visual feedback provided by the opthalmic stereo microscope. Using stereo images, proximity between surgical tools and the retinal surface can be detected when their relative stereo disparity is small. For this purpose, we developed a system comprised of two modules. The first is a module for tracking the surgical tool position on both stereo images. The second is a disparity tracking module for estimating a stereo disparity map of the retinal surface. Both modules were specially tailored for coping with the challenging visualization conditions in retinal surgery. The potential clinical value of the proposed method is demonstrated by extensive testing using a silicon phantom eye and recorded rabbit in vivo data.

Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learning-based method

“…Some other vision-based methods exploit the geometric constraints [8] and the gradientlike features [9,10], in order to identify the shaft of instrument, but fail to provide more accurate 3D positions of the instrument tip. Machine learning techniques [11][12][13][14][15][16][17][18][19] introduced into the instrument detection and tracking provide training of their discriminative classifiers/models according to the input visual features of the foreground (instrument tip or shaft). Edge pixel features [11] and fast corner features [12] are utilized to train the appearance models of surgical instrument based on the likelihood map.…”

“…The Bayesian sequential estimation was also applied to the surgical instrument tracking via the active testing model [18]. Some new metric measurements of image similarity, such as the sum of conditional variance (SCV) [19], have been advanced to improve the performance of instrument detection/tracking. Descriptions of image features and structure model of the surgical instrument are critical play for the MIS instrument tracking.…”

New eye phantom for ophthalmic surgery

“…These structures surround the vitreous body, a transparent gel consisting of water and a network of collagen fibers, hyaluronic acid, and a small amount of soluble proteins. An example of an eye phantom can be found in the research of Richa et al, 7 where it was used to validate a vision-based proximity detection system in retinal surgery. Drugs with specific pharmacological and pharmacokinetic characteristics can be given topically, subconjunctivally, intraocularly, and systemically to treat a variety of ocular diseases.…”

Human eye phantom for developing computer and robot-assisted epiretinal membrane peeling

“…Developments include 3D visualization systems and robust algorithms for tracking the relative motion of the retina and surgical tools [1-3] and for delineating blood vessels. These features would help provide real-time visual feedback during surgery that can additionally be used to generate virtual fixtures with assistive robots [4].…”

“…The development of a microsurgical assistant system for retinal surgery at the Johns Hopkins University has created the need for an eye phantom that combines several features such as ocular geometry, fundus appearance [1-3,5], and realistic ERM peeling behavior, including forces that simulate those encountered during surgery [21]. In this paper, we present an adaptable eye phantom as well as a quantitative assessment of the forces produced during delamination of membranes prepared from four candidate materials for simulating ERM peeling, a standard vitreoretinal microsurgical procedure.…”