Abstract: A processor of an ophthalmologic image processing device acquires an ophthalmologic image photographed by an ophthalmologic image photographing device. The processor inputs the ophthalmologic image into a mathematical model trained by a machine learning algorithm to acquire a result of an analysis relating to at least one of a specific disease and a specific structure of a subject eye. The processor acquires information of a distribution of weight relating to an analysis by a mathematical model, as supplemental distribution information, for which an image area of the ophthalmologic image input into the mathematical model is set as a variable. The processor sets a part of the image area of the ophthalmologic image, as an attention area, based on the supplemental distribution information. The processor acquires an image of a tissue including the attention area among a tissue of the subject eye and displays the image on a display unit.

Abstract: In this invention, a control unit in an ophthalmic image processing device acquires an ophthalmic image captured by an ophthalmic image capture device (S11). The control unit, by inputting the ophthalmic image into a mathematical model that has been trained by a machine-learning algorithm, acquires a probability distribution in which the random variables are the coordinates at which a specific site and/or a specific boundary of a tissue is present within a region of the ophthalmic image (S14). On the basis of the acquired probability distribution, the control unit detects the specific boundary and/or the specific site (S16, S24).

Abstract: In this invention, a control unit in an ophthalmic image processing device acquires an ophthalmic image captured by an ophthalmic image capture device (S11). The control unit, by inputting the ophthalmic image into a mathematical model that has been trained by a machine-learning algorithm, acquires a probability distribution in which the random variables are the coordinates at which a specific site and/or a specific boundary of a tissue is present within a region of the ophthalmic image (S14). On the basis of the acquired probability distribution, the control unit detects the specific boundary and/or the specific site (S16, S24).

Abstract: An ophthalmic system for examining a subject eye of an examinee includes a plurality of examination units, a robot mechanism, and a controller. The plurality of examination units have housings different from each other, perform examinations different from each other, and include at least a first examination unit and a second examination unit. The robot mechanism has a holding unit that holds and releases either the first examination unit or the second examination unit, and a moving unit that is connected to the holding unit and moves three-dimensionally. A controller controls driving of the robot mechanism to adjust a relative positional relationship between the subject eye and the first examination unit or the second examination unit held by the holding unit. The first examination unit or the second examination unit is replaced to be held by the holding unit for performing a different examination on the subject eye.

Abstract: A processor of an ophthalmologic image processing device acquires an ophthalmologic image photographed by an ophthalmologic image photographing device. The processor inputs the ophthalmologic image into a mathematical model trained by a machine learning algorithm to acquire a result of an analysis relating to at least one of a specific disease and a specific structure of a subject eye. The processor acquires information of a distribution of weight relating to an analysis by a mathematical model, as supplemental distribution information, for which an image area of the ophthalmologic image input into the mathematical model is set as a variable. The processor sets a part of the image area of the ophthalmologic image, as an attention area, based on the supplemental distribution information. The processor acquires an image of a tissue including the attention area among a tissue of the subject eye and displays the image on a display unit.

Abstract: A processor of an ophthalmologic image processing device acquires an ophthalmologic image photographed by an ophthalmologic image photographing device. The processor inputs the ophthalmologic image into a mathematical model trained by a machine learning algorithm to acquire a result of an analysis relating to at least one of a specific disease and a specific structure of a subject eye. The processor acquires information of a distribution of weight relating to an analysis by a mathematical model, as supplemental distribution information, for which an image area of the ophthalmologic image input into the mathematical model is set as a variable. The processor sets a part of the image area of the ophthalmologic image, as an attention area, based on the supplemental distribution information. The processor acquires an image of a tissue including the attention area among a tissue of the subject eye and displays the image on a display unit.

Abstract: An OCT signal processing device has an acquisition unit for acquiring three or more OCT signals being temporally different from each other with respect to the same position on a subject, from an OCT device that detects an OCT signal based on reflection light of measurement light applied to a subject and reference light corresponding to the measurement light, and a calculation unit for calculating motion contrast based on a plurality of OCT signals. The calculation unit selects two or more sets of OCT signals having different time interval between the OCT signals among sets obtained by extracting a plurality of OCT signals out of the three or more OCT signals, and calculates motion contrast for each of the selected two or more sets.

Abstract: An OCT apparatus includes an OCT optical system that has a light splitter splitting light from an OCT light source to light travelling to a measurement light path and light travelling to a reference light path and a detector detecting a spectrum interference signal of measurement light guided to a subject eye through the measurement light path and reference light from the reference light path, and a processing unit that processes the spectrum interference signal to generate OCT data. The processing unit performs at least complementary processing on an overlapping region of a real image and a virtual image in OCT data based on a plurality of OCT data obtained with different optical path lengths when detecting the spectrum interference signal, and generates OCT data subjected to the complementary processing.

Abstract: In this invention, a control unit in an ophthalmic image processing device acquires an ophthalmic image captured by an ophthalmic image capture device (S11). The control unit, by inputting the ophthalmic image into a mathematical model that has been trained by a machine-learning algorithm, acquires a probability distribution in which the random variables are the coordinates at which a specific site and/or a specific boundary of a tissue is present within a region of the ophthalmic image (S14). On the basis of the acquired probability distribution, the control unit detects the specific boundary and/or the specific site (S16, S24).

Abstract: The present disclosure relates to an optical coherence tomography (OCT) including: an OCT device, an OCT calculation method, and an OCT calculation program, enabling correlation between images to be easily performed; more specifically it relates to a case where a functional OCT image such as a motion contrast is acquired by using the OCT; acquiring the functional OCT image through a calculation process of an OCT signal is a time-consuming process, for this reason, a long time elapses until the point that a functional OCT image can be observed after OCT imaging is completed, and thus this causes stress to a subject and an examiner; the present disclosure provides an OCT device, an OCT calculation method, and an OCT calculation program, enabling a functional OCT image to be rapidly acquired and enables an examiner to easily perform diagnosis.

Abstract: An OCT apparatus includes an OCT optical system that acquires an OCT signal based on measurement light applied to a subject and reference light, and a processor. The processor controls the OCT optical system based on a predetermined trigger to execute an imaging sequence in which a plurality of temporally different OCT signals are acquired in each of a first region on a subject and a second region adjacent to or partially overlapping with the first region, and imaging conditions are different from each other between the first region and the second region. The processor obtain OCT motion contrast data of the subject based on the plurality of OCT signals acquired through the imaging sequence in the first region and the second region.

Abstract: An ophthalmological image processing apparatus includes an image obtaining portion for obtaining ophthalmological image data, which is captured by an imaging unit, of a subject eye, and an image processing portion for processing the ophthalmological image data. The image processing portion uses a first front image of the subject eye in a first depth region to correct a second front image of the subject eye captured in a second depth region which is different from the first depth region. The first front image and the second front image are constructed based on the ophthalmological image data. The ophthalmological image processing apparatus can reduce an artifact which is generated in an ophthalmological image.

Abstract: A processor of an ophthalmologic image processing device acquires an ophthalmologic image photographed by an ophthalmologic image photographing device. The processor inputs the ophthalmologic image into a mathematical model trained by a machine learning algorithm to acquire a result of an analysis relating to at least one of a specific disease and a specific structure of a subject eye. The processor acquires information of a distribution of weight relating to an analysis by a mathematical model, as supplemental distribution information, for which an image area of the ophthalmologic image input into the mathematical model is set as a variable. The processor sets a part of the image area of the ophthalmologic image, as an attention area, based on the supplemental distribution information. The processor acquires an image of a tissue including the attention area among a tissue of the subject eye and displays the image on a display unit.

Abstract: An optical coherence tomography device provided with: an OCT optical system configured to output OCT signal; and an analysis processing unit configured to process the OCT signal and generate motion contrast data of the specimen. The analysis processing unit is provided with: a first image data generation unit configured to process multiple OCT signals to generate first image data representing phase difference information of the multiple OCT signals, and a second image data generation unit configured to process the OCT signal to generate second image data representing amplitude information of the OCT signal. The analysis processing unit generates the motion contrast data based on the phase difference information in the first image data and the amplitude information in the second image data.

Abstract: An OCT apparatus includes an OCT optical system that has a light splitter splitting light from an OCT light source to light travelling to a measurement light path and light travelling to a reference light path and a detector detecting a spectrum interference signal of measurement light guided to a subject eye through the measurement light path and reference light from the reference light path, and a processing unit that processes the spectrum interference signal to generate OCT data. The processing unit performs at least complementary processing on an overlapping region of a real image and a virtual image in OCT data based on a plurality of OCT data obtained with different optical path lengths when detecting the spectrum interference signal, and generates OCT data subjected to the complementary processing.

Abstract: An OCT signal processing device has an acquisition unit for acquiring three or more OCT signals being temporally different from each other with respect to the same position on a subject, from an OCT device that detects an OCT signal based on reflection light of measurement light applied to a subject and reference light corresponding to the measurement light, and a calculation unit for calculating motion contrast based on a plurality of OCT signals. The calculation unit selects two or more sets of OCT signals having different time interval between the OCT signals among sets obtained by extracting a plurality of OCT signals out of the three or more OCT signals, and calculates motion contrast for each of the selected two or more sets.

Abstract: An OCT apparatus includes an OCT optical system that acquires an OCT signal based on measurement light applied to a subject and reference light, and a processor. The processor controls the OCT optical system based on a predetermined trigger to execute an imaging sequence in which a plurality of temporally different OCT signals are acquired in each of a first region on a subject and a second region adjacent to or partially overlapping with the first region, and imaging conditions are different from each other between the first region and the second region. The processor obtain OCT motion contrast data of the subject based on the plurality of OCT signals acquired through the imaging sequence in the first region and the second region.

Abstract: An ophthalmic analysis device for analyzing OCT motion contrast data including blood vessel information of a subject eye acquired by an ophthalmic OCT, includes: a processor; and memory storing computer readable program, when executed by the processor, causing the ophthalmic analysis device to execute: an analysis process of analyzing the OCT motion contrast data to acquire a measurement result related to a capillary area of the subject eye. The analysis process executes an alleviating process for alleviating the influence of great blood vessels having blood vessel diameters larger than those of capillaries on the measurement result to acquire the measurement result related to the capillary area.

Abstract: An ophthalmological image processing apparatus includes an image obtaining portion for obtaining ophthalmological image data, which is captured by an imaging unit, of a subject eye, and an image processing portion for processing the ophthalmological image data. The image processing portion uses a first front image of the subject eye in a first depth region to correct a second front image of the subject eye captured in a second depth region which is different from the first depth region. The first front image and the second front image are constructed based on the ophthalmological image data. The ophthalmological image processing apparatus can reduce an artifact which is generated in an ophthalmological image.

Abstract: An optical coherence tomography apparatus includes an OCT optical system configured to detect an OCT signal based on measurement light scanned on scan positions of a subject including a blood vessel network by a scanning unit and reference light. The optical coherence tomography apparatus is configured to execute: a signal processing instruction of processing OCT signals which are temporally different from each other with respect to a same position on the subject and generating a motion contrast image which images distribution of a moving object in a depth direction at each of the scan positions based on the OCT signals; and a detecting instruction of analyzing a profile in the depth direction of the motion contrast image generated by the signal processing unit and detecting a change resulting from the blood vessel to detect the blood vessel network included in the subject.