Browsing by Author "Santos, Torcato"
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- Noninvasive evaluation of retinal leakage using optical coherence tomographyPublication . Bernardes, Rui; Santos, Torcato; Serranho, Pedro; Lobo, Conceição; Cunha-Vaz, JoséPurpose: To demonstrate the association between changes in the blood-retinal barrier (BRB) identified by fluorescein leakage and those in the optical properties of the human retina determined by optical coherence tomography (OCT) and show how these changes can be quantified and their location identified within the retina. Methods: Two imaging techniques were applied: the retinal leakage analyzer, to map BRB function into intact or disrupted regions, and OCT, to measure refractive index changes along the light path within the human ocular fundus. Results: A total of 140 comparisons were made, 77 between areas of regions receiving the same classification (intact or disrupted BRB) and 63 between areas of regions receiving distinct classifications, from 4 pathological cases: 2 eyes with nonproliferative diabetic retinopathy and 2 eyes with wet age-related macular degeneration. In all cases, the distribution of OCT data between regions of intact and regions of disrupted BRB, identified by the retinal leakage analyzer, was quantified and was statistically significantly different (p < 0.001). In addition, it was found that the differences could be localized in the retina to specific structural sequences. Conclusions: Using a novel method to analyze OCT data, we showed that it may be possible to quantify differences in the extracellular compartment in eyes with retinal disease and alterations of the BRB. Based on quantitative techniques, our findings demonstrate the presence of indirect information on the BRB status within noninvasive OCT data.
- Ocular fundus Imaging: from structure to functionPublication . Serranho, Pedro; Maduro, Cristina; Santos, Torcato; Bernardes, Rui; Vaz, José Cunha; Araújo, Adérito; Barbeiro, SílviaImaging the ocular fundus, namely the retina, to detect and/or monitor changes over time from the healthy condition is of fundamental importance to assess onset and disease progression and is a valuable tool to understand the basic mechanisms of ocular diseases. Current trends point to the need for less or non-invasive approaches, to the need for detailed (higher spatial and temporal resolution) imaging systems and to the quantification as opposed to qualitative classification of any findings. In this work we present a snapshot of our research by presenting two examples of technical development aiming to obtain structural and function information from the human retina, in vivo, using non-invasive techniques, namely optical coherence tomography imaging. Based on our experience and developed work, we are now starting to bridge the gap to brain imaging as the eye is only the starting point of vision.
- Optical coherence tomography: automatic retina classification through support vector machinesPublication . Bernardes, Rui; Serranho, Pedro; Santos, Torcato; Gonçalves, Valter; Cunha-Vaz, JoséOptical coherence tomography (OCT) is becoming one of the most important imaging modalities in ophthalmology due to its non-invasiveness and by allowing the visualisation the human retina structure in detail. It was recently proposed that OCT data embeds functional information from the human retina. Specifically, it was proposed that blood–retinal barrier status information is present within OCT data from the human retina. Besides this ability, the authors present data supporting the idea of having the OCT data encoding the ageing of the retina in addition to the disease (diabetes) condition from the healthy status. The methodology followed makes use of a supervised classification procedure, the support vector machine (SVM) classifier – based solely on the statistics of the distribution of OCT data from the human retina (i.e. OCT data between the inner limiting membrane and the retinal pigment epithelium). Results achieved suggest that information on both the healthy status of the blood–retinal barrier and on the ageing process co-exist encoded within the optical properties of the human retina.
- Synthetic volume from real optical coherence tomography dataPublication . Serranho, Pedro; Bernardes, Rui; Maduro, Cristina; Santos, Torcato; Cunha-Vaz, JoséPurpose: To build a mathematical model to mimic a real OCT b-scan/volume without noise, in order to establish a ground truth for image processing performance metrics. Methods: Current image processing techniques (eg. despeckling filtering methods) with application to optical coherence tomography (OCT) rely on the respective qualitative evaluation of its results. Quantitative approaches are reduced to using synthetic images which consists of an homogeneous background and a set of abstract objects, eg. cubes and spheres. In this work, we suggest a mathematical model to address this issue by creating a synthetic b-scan/volume based on any real OCT data scan, that can be used as ground truth for processing methods testing. Eye scans of healthy volunteers and eyes of patients with age-related macular degeneration and diabetic retinopathy were used following Cirrus OCT (Carl Zeiss Meditec, Dublin, CA, USA) scans using both the 200x200x1024 and the 512x128x1024 Macular Cube Protocols. Each of these eye scans was processed in order to extract required parameters. For healthy subjects, only the segmentation of the inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) are needed, though for pathologic eyes, the segmentation of other structures to be preserved might also be needed. In each segmented region, OCT data is surface fitted using appropriate basis functions. Results: A total of 45 scans were processed resulting in the synthetic data representing the major characteristics of the respective real OCT scans. These results have been used for testing the performance of an improved complex diffusion despeckling method proposed by some of the authors. Conclusions: This process allows to automatically compute a synthetic OCT scan mimicking a real one. In this way, this process makes it possible to quantify the result of any processing (eg. filtering) by providing adequate synthetic data as ground truth.
- Synthtic OCT Data for image processing performance testingPublication . Serranho, Pedro; Maduro, Cristina; Santos, Torcato; Vaz, José Cunha; Bernardes, RuiThe use of synthetic images is needed for testing the performance of image processing methods in order to establish a ground truth to test performance metrics. However, these synthetic images do not represent real applications. The aim of this paper is to build a mathematical model to obtain a synthetic noise-free image mimicking a real Optical Coherence Tomography (OCT) B-scan or volume from the human retina, in order to establish a ground truth for filtering performance metrics in this context. Moreover we also suggest a method to add speckle noise to this image based on the speckle noise of the given OCT volume. In this way we establish a replicable method to obtain a ground truth for image processing performance metrics that actually mimics a real case.
- Two-dimensional segmentation of the retinal vascular network from optical coherence tomographyPublication . Rodrigues, Pedro; Guimarães, Pedro; Santos, Torcato; Simão, Sílvia; Miranda, Telmo; Serranho, Pedro; Bernardes, RuiThe automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results.