Abstract: An ophthalmic apparatus of an exemplary aspect performs the first and second OCT scans on a subject's eye. The first OCT scan is performed on the first region including the first site of the subject's eye, and the second OCT scan is performed on the second region including the second site. The ophthalmic apparatus acquires the first deviation information of the subject's eye prior to the first OCT scan and performs alignment, and also acquires the second deviation information of the subject's eye prior to the second OCT scan and performs alignment. The ophthalmic apparatus calculates the distance between the first site and the second site based on the first data acquired through the first OCT scan and second data acquired through the second OCT scan.

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 fundus observation apparatus includes an illumination optical system, a two-dimensional image sensor, and a deflecting member. The illumination optical system is configured to illuminate a fundus of a subject's eye with line-shaped illumination light. The two-dimensional image sensor is configured to receive returning light of the illumination light from the fundus on a movable focal plane at a position substantially conjugate optically to the fundus. The deflecting member is configured to couple an optical path of the illumination light and an optical path of the returning light and to scan the fundus with the illumination light by deflecting the illumination light in synchronization with a movement of the focal plane. The deflecting member has a configuration that allows the returning light to be transmitted through a first region and to reflect the illumination light in a second region different from the first region.

Abstract: A medical system according to an aspect example includes a data acquiring unit and a data processor. The data acquiring unit is configured to acquire from a patient two or more types of data among blood oxygen saturation data, auscultatory sound data, ocular image data, and ocular blood flow data. The data processor is configured to process the two or more types of data acquired by the data acquiring unit to detect a conditional change in a circulatory system associated with an infectious disease.

Abstract: According to a medical system of an aspect example, an ophthalmic data acquiring unit includes at least one ophthalmic examination apparatus for acquiring ophthalmic data from a patient, and an internal medicine data acquiring unit includes at least one internal medicine examination apparatus for acquiring internal medicine data from the patient. An inference processor is configured to perform inference processing using a trained model constructed by machine learning using training data that includes ophthalmic data, internal medicine data, and diagnostic result data. The inference processor generates inferred diagnostic data by performing the inference processing based at least on the ophthalmic data of the patient acquired by the ophthalmic data acquiring unit and the internal medicine data of the patient acquired by the internal medicine data acquiring unit. An output unit outputs a result of the inference processing performed by the inference processor.

Abstract: In an ophthalmic system of some embodiments, ophthalmic imaging apparatuses include slit lamp microscopes, and information processing system is connected to each ophthalmic imaging apparatus via a communication path. Each ophthalmic imaging apparatus is configured to acquire a three dimensional image by photographing a subject's eye, and transmit the three dimensional image to the information processing system. The information processing system is configured to receive the three dimensional image, store three dimensional images received, perform machine learning and/or data mining based on the three dimensional images, store knowledge acquired by the machine learning and/or data mining, and generate diagnosis support information by performing inference based on a three dimensional image of a subject's eye transmitted from one of the slit lamp microscopes knowledge stored in the knowledge storage.

Abstract: One aspect example is a system for photogrammetry of a building. A memory stores design data and physical material data. The design data includes virtual material information on attributes for each of virtual materials of a virtual building. The physical material data is on the attributes for each of physical materials and generated based on measured data acquired from a physical building constructed on the basis of the design data. A material associating processor is configured to generate pairs of virtual and physical materials by determining an association between the virtual materials and the physical materials based on the virtual material information and the physical material data. An attribute associating processor is configured to determine an association between the virtual material information and the physical material data in accordance with the attributes for each of the pairs.

Abstract: An ophthalmic apparatus of an exemplary aspect performs the first and second OCT scans on a subject's eye. The first OCT scan is performed on the first region including the first site of the subject's eye, and the second OCT scan is performed on the second region including the second site. The ophthalmic apparatus acquires the first deviation information of the subject's eye prior to the first OCT scan and performs alignment, and also acquires the second deviation information of the subject's eye prior to the second OCT scan and performs alignment. The ophthalmic apparatus calculates the distance between the first site and the second site based on the first data acquired through the first OCT scan and second data acquired through the second OCT scan.

Abstract: An ophthalmic apparatus of an exemplary aspect performs the first and second OCT scans on a subject's eye. The first OCT scan is performed on the first region including the first site of the subject's eye, and the second OCT scan is performed on the second region including the second site. The ophthalmic apparatus acquires the first deviation information of the subject's eye prior to the first OCT scan and performs alignment, and also acquires the second deviation information of the subject's eye prior to the second OCT scan and performs alignment. The ophthalmic apparatus calculates the distance between the first site and the second site based on the first data acquired through the first OCT scan and second data acquired through the second OCT scan.

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: The object of the present invention is to develop an ophthalmologic microscope of a new method that increases the degree of freedom in the optical design in the Galilean ophthalmologic microscope provided with an OCT optical system. The present invention provides an ophthalmologic microscope 1 comprising; an illuminating optical system 300, an observation optical system 400; an objective lens 2; and an OCT optical system 500, characterized in that the optical axis O-500 of the OCT optical system does not penetrate through the objective lens 2, it comprises objective lens for OCT 507 through which the optical axis O-500 of the OCT optical system penetrates, and deflection optical elements 503a, 503b for scanning of the OCT optical system and the objective lens for OCT 507 are in a substantially optically conjugate positional relation.

Abstract: The data set receiving unit 13 of the information processing apparatus 1 of an aspect example receives a data set that includes at least BIM data. The route setting processor 151 sets a route, which is arranged inside and/or outside a virtual building represented by the BIM data, based on the data set received. The virtual image set generating processor 152 generates a virtual image set of the virtual building along the route, based on the received data set and the set route. The inference model creating processor 153 creates an inference model by applying machine learning with training data that includes at least the generated virtual image set to a neural network. The inference model created is used to identify data of a building material from data acquired by measuring a building.

Abstract: The data set receiving unit 13 of the information processing apparatus 1 of an aspect example receives a data set that includes at least BIM data. The route setting processor 151 sets a route, which is arranged inside and/or outside a virtual building represented by the BIM data, based on the data set received. The virtual image set generating processor 152 generates a virtual image set of the virtual building along the route, based on the received data set and the set route. The inference model creating processor 153 creates an inference model by applying machine learning with training data that includes at least the generated virtual image set to a neural network. The inference model created is used to identify data of a building material from data acquired by measuring a building.

Abstract: An exemplary aspect is a method of processing data acquired by applying an optical coherence tomography (OCT) scan to a sample. The method includes preparing a three dimensional data set acquired from a sample, creating a two dimensional map based on representative intensity values of a plurality of pieces of A-scan data included in the three dimensional data set, placing the three dimensional data set based on the two dimensional map, and executing a process based on at least a partial data set of the three dimensional data set on which placement based on the two dimensional map has been performed.

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: The object of the present invention is to develop an ophthalmologic microscope of a new method that increases the degree of freedom in the optical design in the Galilean ophthalmologic microscope provided with an OCT optical system. The present invention provides an ophthalmologic microscope, wherein an observation optical system, an objective lens, and an OCT optical system are placed in such a way that the optical axis of the OCT optical system does not penetrate through objective lens, and the optical axis of the observation optical system and the optical axis of the OCT optical system are non-coaxial, and wherein the ophthalmologic microscope further comprises a SLO optical system that scans a light ray which is a visible ray, a near infrared ray, or an infrared ray and guides the light to the subject's eye so as to become substantially coaxial with the optical axis of the OCT optical system.

Abstract: An OCT imaging method of some exemplary aspects includes acquiring a first three dimensional data set by applying an OCT scan targeting a first three dimensional region of a sample, creating a first two dimensional map based on representative intensity values respectively of a plurality of pieces of A-scan data included in the first three dimensional data set, designating a second three dimensional region of the sample based on the first two dimensional map, acquiring a second three dimensional data set by applying an OCT scan targeting the second three dimensional region, and generating image data from at least part of the second three dimensional data set.

Abstract: The object of the present invention is to develop an ophthalmologic microscope of a new method that increases the degree of freedom in the optical design in the Galilean ophthalmologic microscope provided with an OCT optical system. The present invention provides an ophthalmologic microscope, wherein an observation optical system, an objective lens, and an OCT optical system are placed in such a way that the optical axis of the OCT optical system does not penetrate through objective lens, and the optical axis of the observation optical system and the optical axis of the OCT optical system are non-coaxial, and wherein the ophthalmologic microscope further comprises a SLO optical system that scans a light ray which is a visible ray, a near infrared ray, or an infrared ray and guides the light to the subject's eye so as to become substantially coaxial with the optical axis of the OCT optical system.

Abstract: An ophthalmic apparatus includes an acquiring unit, an OCT unit, an analyzer, and a calculator. The acquiring unit is configured to acquire a dioptric power in a first region including a fovea of a subject's eye. The OCT unit is configured to acquire OCT data of a fundus of the subject's eye using optical coherence tomography. The analyzer is configured to specify a shape of the fundus by analyzing the OCT data. The calculator is configured to calculate a dioptric power in a peripheral region of the first region of the fundus based on the dioptric power in the first region and the shape of the fundus.

Abstract: The data set receiving unit 13 of the information processing apparatus 1 of an aspect example receives a data set that includes at least BIM data. The route setting processor 151 sets a route, which is arranged inside and/or outside a virtual building represented by the BIM data, based on the data set received. The virtual image set generating processor 152 generates a virtual image set of the virtual building along the route, based on the received data set and the set route. The inference model creating processor 153 creates an inference model by applying machine learning with training data that includes at least the generated virtual image set to a neural network. The inference model created is used to identify data of a building material from data acquired by measuring a building.