Agency for Science, Technology and Research

Singapore Health Services Pte Ltd

National University of Singapore

National University Hospital (s) Pte Ltd.

Fengshou Yin

Jiang Liu

Wing Kee Damon Wong

Beng Hai Lee

Ngan Meng Tan

Zhuo Zhang

Jun Cheng

Tien Yin Wong

Tin Aung

Baskaran Mani

Weimin Huang

Joo Hwee Lim

Jing Zhang

Yanwu Xu

Yi Su

Chee Kong Chui

Jiayin Zhou

Huiqi Li

Carol Cheung

Ziyang Liang

Xinting Gao

Ai Ping Yow

Ying Quan

Wing Kee Wong

Stephen Chang

Xiangang Cheng

Ching-Yu Cheng

Yim-lui Carol Cheung

Tao Yang

Liangjing Yang

Kavitha Gopalakrishnan

Kin Yong Chang

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 surgical training system and method. The system comprises means for recording reference data representing a reference manipulation of a computer generated model of an object by a master user; means for physically guiding a trainee user based on the recorded reference data during a training manipulation of the model of the object by the trainee user; and means for recording assessment data representing an assessment manipulation of the model of the object by the trainee user without guidance.

Robot assisted surgical training

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 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

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

Growing community of inventors

Singapore, Singapore

Fengshou Yin