In vivo analysis of the time and spatial activation pattern of microglia in the retina following laser-induced choroidal neovascularization.

Microglia play a major role in retinal neovascularization and degeneration and are thus potential targets for therapeutic intervention. In vivo assessment of microglia behavior in disease models can provide important information to understand patho-mechanisms and develop therapeutic strategies. Although scanning laser ophthalmoscope (SLO) permits the monitoring of microglia in transgenic mice with microglia-specific GFP expression, there are fundamental limitations in reliable identification and quantification of activated cells. Therefore, we aimed to improve the SLO-based analysis of microglia using enhanced image processing with subsequent testing in laser-induced neovascularization (CNV). CNV was induced by argon laser in MacGreen mice. Microglia was visualized in vivo by SLO in the fundus auto-fluorescence (FAF) mode and verified ex vivo using retinal preparations. Three image processing algorithms based on different analysis of sequences of images were tested. The amount of recorded frames was limiting the effectiveness of the different algorithms. Best results from short recordings were obtained with a pixel averaging algorithm, further used to quantify spatial and temporal distribution of activated microglia in CNV. Morphologically, different microglia populations were detected in the inner and outer retinal layers. In CNV, the peak of microglia activation occurred in the inner layer at day 4 after laser, lacking an acute reaction. Besides, the spatial distribution of the activation changed by the time over the inner retina. No significant time and spatial changes were observed in the outer layer. An increase in laser power did not increase number of activated microglia. The SLO, in conjunction with enhanced image processing, is suitable for in vivo quantification of microglia activation. This surprisingly revealed that laser damage at the outer retina led to more reactive microglia in the inner retina, shedding light upon a new perspective to approach the immune response in the retina in vivo.

Retinal image registration as a tool for supporting clinical applications.

BACKGROUND AND OBJECTIVE: The study of small vessels allows for the analysis and diagnosis of diseases with strong vasculopathy. This type of vessels can be observed non-invasively in the retina via fundoscopy. The analysis of these vessels can be facilitated by applications built upon Retinal Image Registration (RIR), such as mosaicing, Super Resolution (SR) or eye shape estimation. RIR is challenging due to possible changes in the retina across time, the utilization of diverse acquisition devices with varying properties, or the curved shape of the retina. METHODS: We employ the Retinal Image Registration through Eye Modelling and Pose Estimation (REMPE) framework, which simultaneously estimates the cameras' relative poses, as well as eye shape and orientation to develop RIR applications and to study their effectiveness. RESULTS: We assess quantitatively the suitability of the REMPE framework towards achieving SR and eye shape estimation. Additionally, we provide indicative results demonstrating qualitatively its usefulness in the context of longitudinal studies, mosaicing, and multiple image registration. Besides the improvement over registration accuracy, demonstrated via registration applications, the most important novelty presented in this work is the eye shape estimation and the generation of 3D point meshes. This has the potential for allowing clinicians to perform measurements on 3D representations of the eye, instead of doing so in 2D images that contain distortions induced because of the projection on the image space. CONCLUSIONS: RIR is very effective in supporting applications such as SR, eye shape estimation, longitudinal studies, mosaicing and multiple image registration. Its improved registration accuracy compared to the state of the art translates directly in improved performance when supporting the aforementioned applications.

REMPE: Registration of Retinal Images Through Eye Modelling and Pose Estimation.

OBJECTIVE: In-vivo assessment of small vessels can promote accurate diagnosis and monitoring of diseases related to vasculopathy, such as hypertension and diabetes. The eye provides a unique, open, and accessible window for directly imaging small vessels in the retina with non-invasive techniques, such as fundoscopy. In this context, accurate registration of retinal images is of paramount importance in the comparison of vessel measurements from original and follow-up examinations, which is required for monitoring the disease and its treatment. At the same time, retinal registration exhibits a range of challenges due to the curved shape of the retina and the modification of imaged tissue across examinations. Thereby, the objective is to improve the state-of-the-art in the accuracy of retinal image registration. METHOD: In this work, a registration framework that simultaneously estimates eye pose and shape is proposed. Corresponding points in the retinal images are utilized to solve the registration as a 3D pose estimation. RESULTS: The proposed framework is evaluated quantitatively and shown to outperform state-of-the-art methods in retinal image registration for fundoscopy images. CONCLUSION: Retinal image registration methods based on eye modelling allow to perform more accurate registration than conventional methods. SIGNIFICANCE: This is the first method to perform retinal image registration combined with eye modelling. The method improves the state-of-the-art in accuracy of retinal registration for fundoscopy images, quantitatively evaluated in benchmark datasets annotated with ground truth. The implementation of registration method has been made publicly available.

An experimental evaluation of the accuracy of keypoints-based retinal image registration.

This work regards an investigation of the accuracy of a state-of-the-art, keypoint-based retinal image registration approach, as to the type of keypoint features used to guide the registration process. The employed registration approach is a local method that incorporates the notion of a 3D retinal surface imaged from different viewpoints and has been shown, experimentally, to be more accurate than competing approaches. The correspondences obtained between SIFT, SURF, Harris-PIIFD and vessel bifurcations are studied, either individually or in combinations. The combination of SIFT features with vessel bifurcations was found to perform better than other combinations or any individual feature type, alone. The registration approach is also comparatively evaluated against representative methods of the state-of-the-art in retinal image registration, using a benchmark dataset that covers a broad range of cases regarding the overlap of the acquired images and the anatomical characteristics of the imaged retinas.

Retinal image registration under the assumption of a spherical eye.

We propose a method for registering a pair of retinal images. The proposed approach employs point correspondences and assumes that the human eye has a spherical shape. The image registration problem is formulated as a 3D pose estimation problem, solved by estimating the rigid transformation that relates the views from which the two images were acquired. Given this estimate, each image can be warped upon the other so that pixels with the same coordinates image the same retinal point. Extensive experimental evaluation shows improved accuracy over state of the art methods, as well as robustness to noise and spurious keypoint matches. Experiments also indicate the method's applicability to the comparative analysis of images from different examinations that may exhibit changes and its applicability to diagnostic support.

Retinal image registration through simultaneous camera pose and eye shape estimation.

In this paper, a retinal image registration method is proposed. The approach utilizes keypoint correspondences and assumes that the human eye has a spherical or ellipsoidal shape. The image registration problem amounts to solving a camera 3D pose estimation problem and, simultaneously, an eye 3D shape estimation problem. The camera pose estimation problem is solved by estimating the relative pose between the views from which the images were acquired. The eye shape estimation problem parameterizes the shape and orientation of an ellipsoidal model for the eye. Experimental evaluation shows 17.91% reduction of registration error and 47.52% reduction of the error standard deviation over state of the art methods.

Retinal image registration based on keypoint correspondences, spherical eye modeling and camera pose estimation.

In this work, an image registration method for two retinal images is proposed. The proposed method utilizes keypoint correspondences and assumes a spherical model of the eye. Image registration is treated as a pose estimation problem, which requires estimation of the rigid transformation that relates the two images. Using this estimate, one image can be warped so that it is registered to the coordinate frame of the other. Experimental evaluation shows improved accuracy over state-of-the-art approaches as well as robustness to noise and spurious keypoint correspondences. Experiments also indicate the method's applicability to diagnostic image enhancement and comparative analysis of images from different examinations.

Super resolution for fundoscopy based on 3D image registration.

An approach to the generation of super-resolution (SR) images from fundoscopy images is proposed that is based on the 3D registration of the original fundoscopy images. The proposed approach utilizes a simple 3D registration method to enable the application of conventional SR techniques which, otherwise, employ 2D image registration. Qualitative and quantitative comparative evaluation shows that the obtained results improve image definition and alleviate noise.