Kabushiki Kaisha Topcon

Topcon Corporation

Osaka University

Kinpui Chan

Zhenguo Wang

Yasufumi Fukuma

Masahiro Akiba

Hisashi Tsukada

Zaixing Mao

Jongsik Kim

Hiroyuki Otsuka

Song Mei

Zhijia Yuan

Jonathan Liu

Toshihiro Mino

Bin Cao

Dawei Li

Charles A Reisman

Kazuhiko Yumikake

Ying Dong

Kohji Nishida

Richard F Spaide

Kazuichi Maruyama

Ryo Kawasaki

Chikako Hara

Atsuya Miki

Hirokazu Sakaguchi

Xin Sui

Kim Jongsik

A medical diagnostic apparatus includes a receiver circuit that receives three-dimensional volumetric data of a subject's eye, and a processor configured to separate portions of the three-dimensional volumetric data into separate segments, perform processing differently on each of the separate segments, and combine the separately processed segments to produce an enhanced three-dimensional volumetric data set. The processor is further configured to generate at least one diagnostic metric from the enhanced three-dimensional volumetric data set, and the processor is further configured to evaluate a pathological condition based on the at least one diagnostic metric. Related methods and computer readable media are also disclosed.

Medical diagnostic apparatus and method for evaluation of pathological conditions using 3D optical coherence tomography data and images

A method for thickness measurement of a multi-layer object combines frequency analysis and a theoretical model. The frequency analysis is used to determine the thickness of a thicker layer of the object. The theoretical model is used to determine the thickness of the thinner layer of the object.

2D multi-layer thickness measurement

An ophthalmological imaging device includes illumination optics having an illumination light source that outputs an illumination light, a scanner that redirects the illumination light toward a portion of an object to be imaged, and an optical image capture device including a camera that receives backscattered light that is scattered from the illumination light by the object and captures first and second images of the backscattered light. The device also includes a control processor that controls the scanner and the optical image capture device to cause the optical image capture device to capture the first and second images of the object. The first and second images are captured by the camera at different times and extracted from different portions of the backscattered light. The device also includes an image processor that generates a stereoscopic image from the first and second images of the object.

Method and apparatus for stereoscopic color eye imaging

An ophthalmic apparatus includes an illumination optical system, an imaging optical system, and a controller. The illumination optical system is configured to generate illumination light using light from a light source, and to illuminate a changeable illumination region on a predetermined site of a subject's eye with the illumination light having a light intensity corresponding to a size of the illumination region. The imaging optical system is configured to guide returning light of the illumination light from the subject's eye to a light receiving surface of an image sensor. The controller is configured to control the image sensor to set an opening range so as to overlap an illumination range of the returning light on the light receiving surface corresponding to the illumination region and to capture a light receiving result obtained by a light receiving element in the set opening range.

Ophthalmic apparatus, method of controlling same, and recording medium

Optical coherence tomography (OCT) angiography (OCTA) data is generated by one or more machine learning systems to which OCT data is input. The OCTA data is capable of visualization in three dimensions (3D) and can be generated from a single OCT scan. Further, motion artifact can be removed or attenuated in the OCTA data by performing the OCT scans according to special scan patterns and/or capturing redundant data, and by the one or more machine learning systems.

Method and apparatus for optical coherence tomography angiography

A low coherence imaging method comprises acquiring image data of an object with an interferometric imaging system, where the image data is from a location of the object at first and second times; determining a first depth profile from the image data from the location at the first time and a second depth profile from the image data of the location at the second time; determining a change with respect to depth between the first and second depth profiles; and determining a property, or identifying a location, of at least one dynamic particle in the object based on the change between the first and second depth profiles. The method is able to identify, analyze, and/or visualize dynamic particles with features comparable to at least fluorescein angiography, indocyanine green angiography, confocal scanning laser fluorescein angiography, confocal scanning laser indocyanine green angiography, and fluorescence microscopy images, without the use of a dye.

Method and apparatus for low coherence interferometry

A method for determining thickness of layers of the tear film includes reconstructing a full- or hyper-spectral interference pattern from an imaged multi-spectral pattern. Tear film thickness can then be estimated from the full- or hyper-spectral interference pattern. Using a full- or hyper-spectral interference pattern provides a greater number of frequency sampling points for increased tear film thickness estimation accuracy, without traditional time consuming techniques.

2D multi-layer thickness measurement with reconstructed spectrum

An interferometric method of identifying the thickness of an object that is too thin to be resolved by a Fourier transform of the interference signal includes applying a harmonic frequency modulation to an envelope of the interference signal. Where the object is a tear film, this method may be utilized to determine a thickness of the lipid layer of the tear film.

Method and apparatus for measuring tear film thickness using optical interference

Shadows caused by opacities and obstructions (such as blood vessels) can block detection of structural information of objects below the shadow-causing structure, affecting visualization and analysis of those deeper structures. An image processing method for removing these shadows includes applying a low pass filter to an energy profile of an image to remove high frequency fluctuations in energy, thereby effectively recovering lower energy inside shadow regions and removing high frequency speckle. The image is then adjusted, for example by linear scaling, based on the filtered energy profile. Two dimensional filters may also be applied for volumes.

3D shadow reduction signal processing method for optical coherence tomography (OCT) images

A machine learning model is trained to identify the texture difference between the different layers of a multilayer object. By training with data in full 3D space, the resulting model is capable of predicting the probability that each pixel in a 3D image belongs to a certain layer. With the resulting probability map, comparing probabilities allows one to determine boundaries between layers, and/or other properties and useful information such as volume data.

3D quantitative analysis with deep learning

Ophthalmological images generated by coherent imaging modalities have multiple types of noise, including random noise caused by the imaging system and speckle noise caused by turbid objects such as living tissues. These noises can occur at different levels in different locations. A noise-reduction method and system of the present disclosure thus relates to applying different filters for different types of noise and/or different locations of images, sequentially or in parallel and combined, to produce a final noise-reduced image.

Image quality improvement methods for optical coherence tomography

A multimodal imaging system and method is capable of taking fundus images, automatically identifying regions of interest (ROIs) of the eye from the fundus images, and performing OCT imaging in the identified ROIs, where the OCT images can provide clinically relevant information for screening purposes. By automatically identifying the ROIs, expert intervention is not required to perform specialized OCT imaging and thus, such imaging and analysis can be provided at more facilities and for more subjects for a lower cost.

Automatically identifying regions of interest of an object from horizontal images using a machine learning guided imaging system

A method for measuring layer thicknesses of a multi-layer structure includes generating first and second 2D images of the structure, each image being generated by measuring an intensity of a reflection of incident lights, from different discrete narrow spectral bands, on the structure. Thicknesses for at least one layer of the structure are then determined based on the measured reflection intensities at those locations.

3D quantitative analysis of retinal layers with deep learning

An optical coherence tomography (OCT) system electrically mixes a signature signal with an OCT signal (e.g., an interferogram) output by a photodetector of the OCT system. The signature signal may be a signal output by a photodetector that detects an optical signal from a fiber Bragg grating. The signature signal may then be time delayed before combination with the OCT signal. A series of interferograms are then aligned according to the signature signal. A rescaling signal may be similarly electrically mixed with the signature and OCT signals.

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Ridgewood, NJ, United States of America

Kinpui Chan