Agency for Science, Technology and Research

Singapore Health Services Pte Ltd

Kabushiki Kaisha Topcon

National University of Singapore

Tan Tock Seng Hospital Pte Ltd

National Skin Centre (Singapore) Pte Ltd

National University Hospital (S) Pte Ltd.

Wing Kee Damon Wong

Jiang Liu

Jun Cheng

Beng Hai Lee

Ngan Meng Tan

Fengshou Yin

Yanwu Xu

Tien Yin Wong

Zhuo Zhang

Tin Aung

Baskaran Mani

Joo Hwee Lim

Huiqi Li

Masahiro Akiba

Carol Cheung

Ziyang Liang

Ai Ping Yow

Weimin Huang

Jing Zhang

Jodhbir Singh Mehta

Yi Su

Jiayin Zhou

Shamira Asith Perera

Xinting Gao

Chee Kong Chui

Tiang Hwee Donald Tan

Hong Liang Tey

Ying Quan

Tock Han Lim

Ruchir Srivastava

Yim-lui Carol Cheung

Huiying Liu

Tao Yang

Liangjing Yang

Kin Yong Chang

Augustinus Laude

Kavitha Gopalakrishnan

Lixin Duan

Annan Li

Xiangang Cheng

Carolin Wall

Ching-Yu Cheng

The present disclosure generally relates to automated method and system for vision assessment of a subject. The method comprises: determining a set of test patterns for the subject based on a preliminary assessment of an eye of the subject; displaying the set of test patterns sequentially to the subject; collecting data on the subject's gaze in response to each test pattern displayed to the subject; and assessing vision functionality of the subject based on the collected gaze data.

Vision assessment based on gaze

The present disclosure generally relates to an automated method and system for generating a three-dimensional (3D) representation of a skin structure of a subject. The method comprises: acquiring a plurality of two-dimensional (2D) cross-sectional images of the skin structure, specifically, using optical coherence tomography (OCT) technique; computing a cost for each 2D cross-sectional image based on a cost function, the cost function comprising an edge-based parameter and a non-edge-based parameter; constructing a 3D graph from the 2D cross-sectional images; and determining a minimum-cost closed set from the 3D graph based on the computed costs for the 2D cross-sectional images, wherein the 3D representation of the skin structure is generated from the minimum-cost closed set.

Three-dimensional representation of skin structure

An optical coherence tomography (OCT) image composed of a plurality of A-scans of a structure is analyzed by defining, for each A-scan, a set of neighboring A-scans surrounding the A-slices scan. Following an optional de-noising step, the neighboring A-scans are aligned in the imaging direction, then a matrix X is formed from the aligned A-scans, and matrix completion is performed to obtain a reduced speckle noise image.

Speckle reduction in optical coherence tomography images

A method of assessing the quality of an retinal image (such as a fundus image) includes selecting at least one region of interest within a retinal image corresponding to a particular structure of the eye (e.g. the optic disc or the macula), and a quality score is calculated in respect of the, or each, region-of-interest. Each region of interest is typically one associated with pathology, as the optic disc and the macula are. Optionally, a quality score may be calculated also in respect of the eye as a whole (i.e. over the entire image, if the entire image corresponds to the retina).

Methods and systems for assessing retinal images, and obtaining information from retinal images

A method and system are proposed to obtain a reduced speckle noise image of a subject from optical coherence tomography (OCT) image data of the subject. The cross sectional images each comprise a plurality of scan lines obtained by measuring the time delay of light reflected, in a depth direction, from optical interfaces within the subject. The method comprises two aligning steps. First the cross sectional images are aligned, then image patches of the aligned cross sectional images are aligned to form a set of aligned patches. An image matrix is then formed from the aligned patches; and matrix completion is applied to the image matrix to obtain a reduced speckle noise image of the subject.

Reducing speckle noise in optical coherence tomography images

A numerical parameter indicative of the degree of match between two retina images is produced by comparing two graphs obtained from the respective images. Each graph is composed of edges and vertices. Each vertex is associated with a location the corresponding retina image, and with descriptor data describing a part of the corresponding retina image proximate the corresponding location.

Robust graph representation and matching of retina images

A method is proposed for automatically locating the optic disc or the optic cup in an image of the rear of an eye. A portion of the image containing the optic disc or optic cup is divided into sub-regions using a clustering algorithm. Biologically inspired features, and optionally other features, are obtained for each of the sub-regions. An adaptive model uses the features to generate data indicative of whether each sub-region is within or outside the optic disc or optic cup. The result is then smoothed, to form an estimate of the position of the optic disc or optic cup.

Methods and systems for automatic location of optic structures in an image of an eye, and for automatic retina cup-to-disc ratio computation

A method is presented to obtain, from a retinal image, data characterizing the optic cup, such as data indicating the location and/or size of the optic cup in relation to the optic disc. A disc region of the retinal image of an eye, is expressed as a weighted sum of a plurality of pre-existing 'reference' retinal images in a library, with the weights being chosen to minimize a cost function. The data characterizing the cup of the eye is obtained from cup data associated with the pre-existing disc images and the corresponding weights. The cost function includes (i) a construction error term indicating a difference between the disc region of the retinal image and a weighted sum of the reference retinal images, and (ii) a cost term, which may be generated using a weighted sum over the reference retinal images of a difference between the reference retinal images and the disc region of the retinal image.

Cost-sensitive linear reconstruction based optic cup localization

A method is proposed for analyzing an optical coherence tomography (OCT) image of the anterior segment (AS) of a subject's eye. A region of interest is defined which is a region of the image containing the junction of the cornea and iris, and an estimated position the junction within the region of interest is derived. Using this a second region of the image is obtained, which is a part of the image containing the estimated position of the junction. Features of the second region are obtained, and those features are input to an adaptive model to generate data characterizing the junction.

Methods and systems for characterizing angle closure glaucoma for risk assessment or screening

A non-stereo fundus image is used to obtain a plurality of glaucoma indicators. Additionally, genome data for the subject is used to obtain genetic marker data relating to one or more genes and/or SNPs associated with glaucoma. The glaucoma indicators and genetic marker data are input into an adaptive model operative to generate an output indicative of a risk of glaucoma in the subject. In combination, the genetic indicators and genome data are more informative about the risk of glaucoma than either of the two in isolation. The adaptive model may be a two-stage model, having a first stage in which individual genetic indicators are combined with respective portions of the genome data by first adaptive model modules to form respective first outputs, and a second stage in which the first outputs are combined by a second adaptive mode. Texture analysis is performed on the fundus images to classify them based on their quality, and only images which are determined to meet a quality criterion are subjected to an analysis to determine if they exhibit glaucoma indicators. Also, the images are put into a standard format. The system may include estimating the position of the optic cup by combining results from multiple optic cup segmentation techniques. The system may include estimating the position of the optic disc by applying edge detection to the funds image, excluding edge points that are unlikely to be optic disc boundary points, and estimating the position of an optic disc by fitting an ellipse to the remaining edge points.

Obtaining data for automatic glaucoma screening, and screening and diagnostic techniques and systems using the data

An OCT image of an eye which has been subject to a DSAEK corneal transplant, in which a Descement's membrane in the cornea has been replaced by a graft, is processed to identify the outline of the graft. The process includes the steps of: computationally extracting the boundary of the cornea including the graft; computationally detecting the corners of the graft; computationally extracting points on the boundary between the graft and the original cornea; and computationally fitting the points on the boundary between the graft and the original cornea smoothly into a curve. The outline of the graft is then displayed. A graft profile may be generated, indicating the thickness of the graft at each point along its length.

Corneal graft evaluation based on optical coherence tomography image

A robotic device for use in image-guided robot assisted surgical training, the robotic device comprising a manual interface structure configured to simulate handling of a surgical tool; a translational mechanism for translational motion of the manual interface structure; a rotational mechanism for rotational motion of the manual interface structure; and a spherical mechanism configured to decouple the orientation of the manual interface structure into spatial coordinates, wherein a linkage between the rotational mechanism, the rotational mechanism and the spherical mechanism, and the manual interface structure are disposed on opposing sides of an intersection of a pitch axis and a yaw axis of the spherical mechanism.

Robotic device for use in image-guided robot assisted surgical training

A method for determining the position of an optic cup boundary in a 2D retinal image. The method includes detecting kinks in blood vessels at an estimated boundary of the optic cup and the optic disc, and determining the position of the optic cup boundary in the 2D retinal image based on the detected kinks. The determined optic cup boundary may be used for determining a CDR which may in turn be used for determining a risk of contracting glaucomatous.

Method and system for determining the position of an optic cup boundary

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Singapore, Singapore

Wing Kee Damon Wong