Dataset Information

EyeSLAM: Real-time simultaneous localization and mapping of retinal vessels during intraocular microsurgery.

ABSTRACT: BACKGROUND:Fast and accurate mapping and localization of the retinal vasculature is critical to increasing the effectiveness and clinical utility of robot-assisted intraocular microsurgery such as laser photocoagulation and retinal vessel cannulation. METHODS:The proposed EyeSLAM algorithm delivers 30 Hz real-time simultaneous localization and mapping of the human retina and vasculature during intraocular surgery, combining fast vessel detection with 2D scan-matching techniques to build and localize a probabilistic map of the vasculature. RESULTS:In the harsh imaging environment of retinal surgery with high magnification, quick shaky motions, textureless retina background, variable lighting and tool occlusion, EyeSLAM can map 75% of the vessels within two seconds of initialization and localize the retina in real time with a root mean squared (RMS) error of under 5.0 pixels (translation) and 1° (rotation). CONCLUSIONS:EyeSLAM robustly provides retinal maps and registration that enable intelligent surgical micromanipulators to aid surgeons in simulated retinal vessel tracing and photocoagulation tasks.

SUBMITTER: Braun D

PROVIDER: S-EPMC5762412 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Publications

EyeSLAM: Real-time simultaneous localization and mapping of retinal vessels during intraocular microsurgery.

Braun Daniel D Yang Sungwook S Martel Joseph N JN Riviere Cameron N CN Becker Brian C BC

The international journal of medical robotics + computer assisted surgery : MRCAS 20170718 1

<h4>Background</h4>Fast and accurate mapping and localization of the retinal vasculature is critical to increasing the effectiveness and clinical utility of robot-assisted intraocular microsurgery such as laser photocoagulation and retinal vessel cannulation.<h4>Methods</h4>The proposed EyeSLAM algorithm delivers 30 Hz real-time simultaneous localization and mapping of the human retina and vasculature during intraocular surgery, combining fast vessel detection with 2D scan-matching techniques to ...[more]

PMID: 28719002

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Project description:PURPOSE:The implementation of motion management techniques in radiation therapy can aid in mitigating uncertainties and reducing margins. For motion management to be effective, it is necessary to track key structures both accurately and at a real-time speed. Therefore, the focus of this work was to develop a 2D algorithm for the real-time tracking of ultrasound features to aid in radiation therapy motion management. MATERIALS AND METHODS:The developed algorithm utilized a similarity measure-based block matching algorithm incorporating training methods and multiple simultaneous templates. The algorithm is broken down into three primary components, all of which use normalized cross-correlation (NCC) as a similarity metric. First, a global feature shift to account for gross displacements from the previous frame is determined using large block sizes which encompass the entirety of the feature. Second, the most similar reference frame is chosen from a series of training images that are accumulated during the first K frames of tracking to aid in contour consistency and provide a starting point for the localized template initialization. Finally, localized block matching is performed through the simultaneous use of both a training frame and the previous frame. The localized block matching utilizes a series of templates positioned at the boundary points of the training and previous contours. The weighted final boundary points from both the previous and the training frame are ultimately combined and used to determine an affine transformation from the previous frame to the current frame. RESULTS:A mean tracking error of 0.72 ± 1.25 mm was observed for 85 point-landmarks across 39 ultrasound sequences relative to manual ground truth annotations. The image processing speed per landmark with the GPU implementation was between 41 and 165 frames per second (fps) during the training set accumulation, and between 73 and 234 fps after training set accumulation. Relative to a comparable multithreaded CPU approach using OpenMP, the GPU implementation resulted in speedups between -30% and 355% during training set accumulation, and between -37% and 639% postaccumulation. CONCLUSIONS:Initial implementations indicated an accuracy that was comparable to or exceeding those achieved by alternative 2D tracking methods, with a computational speed that is more than sufficient for real-time applications in a radiation therapy environment. While the overall performance reached levels suitable for implementation in radiation therapy, the observed increase in failures for smaller features, as well as the algorithm's inability to be applied to nonconvex features warrants additional investigation to address the shortcomings observed.

Project description:Real-time monitoring of the brain is potentially valuable for performance monitoring, communication, training or rehabilitation. In natural situations, the brain performs a complex mix of various sensory, motor or cognitive functions. Thus, real-time brain monitoring would be most valuable if (a) it could decode information from multiple brain systems simultaneously, and (b) this decoding of each brain system were robust to variations in the activity of other (unrelated) brain systems. Previous studies showed that it is possible to decode some information from different brain systems in retrospect and/or in isolation. In our study, we set out to determine whether it is possible to simultaneously decode important information about a user from different brain systems in real time, and to evaluate the impact of concurrent activity in different brain systems on decoding performance.We study these questions using electrocorticographic signals recorded in humans. We first document procedures for generating stable decoding models given little training data, and then report their use for offline and for real-time decoding from 12 subjects (six for offline parameter optimization, six for online experimentation). The subjects engage in tasks that involve movement intention, movement execution and auditory functions, separately, and then simultaneously. Main Results: Our real-time results demonstrate that our system can identify intention and movement periods in single trials with an accuracy of 80.4% and 86.8%, respectively (where 50% would be expected by chance). Simultaneously, the decoding of the power envelope of an auditory stimulus resulted in an average correlation coefficient of 0.37 between the actual and decoded power envelopes. These decoders were trained separately and executed simultaneously in real time.This study yielded the first demonstration that it is possible to decode simultaneously the functional activity of multiple independent brain systems. Our comparison of univariate and multivariate decoding strategies, and our analysis of the influence of their decoding parameters, provides benchmarks and guidelines for future research on this topic.

Dataset Information

EyeSLAM: Real-time simultaneous localization and mapping of retinal vessels during intraocular microsurgery.

Publications

EyeSLAM: Real-time simultaneous localization and mapping of retinal vessels during intraocular microsurgery.

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