Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A medical diagnostic apparatus includes a receiver circuit that receives three-dimensional data of a subject's eye, and a processor configured to separate portions of the three-dimensional data into separate segments, perform processing differently on each of the separate segments, and combine the separately processed segments to produce a segmented three-dimensional data set. The processor is further configured to generate at least one diagnostic metric from the segmented three-dimensional 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.

Abstract: 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.

Abstract: In an ophthalmologic apparatus, including: an objective lens that faces a subject's eye; an illumination optical system that irradiates a cornea of the subject's eye with illumination light through the objective lens; and a corneal measurement optical system including an interference image capturing camera that takes an image of a corneal reflection light, which is a reflection of the illumination light reflected from the cornea, through the objective lens, a numerical aperture G of the illumination optical system is larger than a numerical aperture g of the corneal measurement optical system.

Abstract: An ophthalmologic apparatus includes: an objective lens that faces a subject's eye; a first illumination optical system that irradiates a cornea of the subject's eye with illumination light; and a corneal measurement optical system having an imaging element that takes an image of a corneal reflection light, which is a reflection of the illumination light, through the objective lens, and outputs an imaging signal. The corneal measurement optical system includes a first mirror arranged near the objective lens and a second mirror arranged near the imaging element. The first and second mirrors and are configured such that the corneal reflection light that enters and is reflected from the first mirror, and then enters and is reflected from the second mirror exits toward an incident side from which the corneal reflection light enters a reflection surface of the first mirror.

Abstract: A medical diagnostic apparatus includes a receiver circuit that receives three-dimensional data of an eye, and processing circuitry configured to segment the three-dimensional data into regions that include a target structural element and regions that do not include the target structural element to produce a segmented three-dimensional data set. The segmenting is performed using a plurality of segmentation algorithms. Each of the plurality of segmentation algorithms is trained separately on different two-dimensional data extracted from the three-dimensional data. The processing circuitry is further configured to generate at least one metric from the segmented three-dimensional data set, and evaluate a medical condition based on the at least one metric.

Abstract: An ophthalmologic apparatus includes: an objective lens 18 configured to face a subject's eye E; an illumination optical system 1c configured to irradiate the subject's eye E with illumination light L1; a measurement optical system 1 b configured to take an interference image of corneal reflection light R1, which is a reflection of the illumination light L1, through the objective lens 18; an observation optical system 1a configured to image an anterior segment of the subject's eye E through the objective lens 18; a control unit 2 configured to process information on imaging by the measurement optical system 1b and the observation optical system 1 a; and the control unit 2 configured to simultaneously output, to a single output unit 3, tear film information calculated from the interference image by the measurement optical system 1 b, and information on the anterior segment E imaged by the observation optical system 1a.

Abstract: 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.

Abstract: An ophthalmologic apparatus includes: an objective lens that faces a subject's eye; an alignment light emitting unit that irradiates the subject's eye with alignment light to perform measurement of alignment between the objective lens and the subject's eye; an alignment reflection light receiving unit that receives alignment reflection light, which is a reflection of the alignment light from the subject's eye; an anterior segment camera that receives corneal reflection light; and a holder positioned between the anterior segment camera and the objective lens to hold the anterior segment camera, wherein the holder includes an imaging transmissive portion that transmits the corneal reflection light to the anterior segment camera, a first light-transmissive portion that transmits the alignment light from the alignment light emitting unit, and a second light-transmissive portion that transmits the alignment reflection light from the subject's eye.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An ophthalmologic apparatus includes: an ophthalmologic apparatus body having: an objective lens that faces a subject's eye; a first illumination optical system that irradiates a cornea of the subject's eye with illumination light emitted from a first illumination light source along an optical axis overlapping an optical axis center of the objective lens; an interference image capturing camera that takes an image of a corneal reflection light through the objective lens and outputs an imaging signal; and a calculation unit that calculates, based on a corneal reflection image, of a corneal reflection light, input from the interference image capturing camera, a thickness of a tear fluid film at each position on the corneal surface; and a guide rail that supports the ophthalmologic apparatus body.

Abstract: 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.

Abstract: A method for generating clinically valuable analyses and visualizations of 3D volumetric OCT data by combining a plurality of segmentation techniques of common OCT data in three dimensions following pre-processing techniques. Prior to segmentation, the data may be subject to a plurality of separately applied pre-processing techniques.

Abstract: 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.