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 scanning apparatus includes a scanner configured to move a scanning spot along a movement path to capture three-dimensional information of a target object. The movement path includes a plurality of b-scan paths performed along a c-scan path. The scanning apparatus also includes a processing circuit configured as a controller to control the movement path of the scanning spot. An area of the target object covered by the movement path may be independent of a length of the b-scan path. The processing circuit may be configured as an analyzer configured to determine a saturation time for each three-dimensional position within a portion of the target object based on an inter-scan time.

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: 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.

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: 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.

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: 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: A telemedicine data transfer system receives telemedicine data from a data source and decides whether to transmit the telemedicine data to a remote end circuit or prevent transmission of the telemedicine data to the remote end circuit based on a result of an evaluation. A telemedicine data transfer system includes image compression and decompression circuits. The decompression circuit may produce decompressed and enhanced telemedicine data. The image compression and decompression circuits include neural networks trained using an objective function to evaluate a difference between a training input data provided to the image compression circuit and a training output data that is output from the image decompression circuit. The training output data is made different from the training input data by a data enhancement operation performed on the training output data prior to the evaluation.

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.

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.