Abstract: A medical image processing device processes tomographic image data of a living tissue. The medical image processing device includes a control unit that includes at least one processor and at least one memory storing computer program code. The computer program code, when executed by the at least one processor, causes the at least one processor to: acquire a tomographic image in which a layer of the living tissue appears; perform a tilt-reduction process on the acquired tomographic image to reduce a tilt of the layer of the living tissue with respect to a main direction; and acquire medical data by inputting, into a mathematical model, a tilt-reduced image that is the tomographic image on which the tilt-reduction process was performed.

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 OCT device includes a controller configured to: acquire OCT data of a tissue of a subject in a depth direction that is generated by processing interference signal detected by a light receiving element; and perform a correction process to reduce an effect by a noise floor in the OCT data using a plurality of correction amounts each of which is set in accordance with depth at a corresponding data acquisition position. Strength of the noise floor varies depending on the depth of the data acquisition position.

Abstract: Provided are ophthalmologic image processing method, including an acquisition step of acquiring OCT data of an eye to be examined based on a spectral interference signal output from an OCT optical system, a setting step of setting a depth region including an image position of a tissue as an extraction region for data on one-direction side from a zero delay position in the OCT data, and a display control step of extracting extracted OCT data corresponding to the extraction region from the OCT data and displaying the extracted OCT data in a display region set in advance on a monitor, and an OCT apparatus that executes the method.

Abstract: A medical image processing device is configured to process data of a three-dimensional image of a biological tissue. The medical image processing device includes a controller configured to: acquire, as an image acquisition step, a three-dimensional image of a tissue; extract, as an extraction step, a first region from the acquired three-dimensional image, the first region being a part of the three-dimensional image; and acquire, as a first structure detection step, a detection result of a specific structure of the tissue in the extracted first region by inputting the first region into a mathematical model that is trained by a machine learning algorithm to output a detection result of a specific structure that is shown in an image input into the mathematical model.

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: A fundus image processing apparatus including a controller configured to perform image acquisition processing of acquiring a three-dimensional tomographic image of the fundus of the subject eye, reference position setting processing of setting a reference position in a region of an optic nerve head in the two-dimensional measurement region in which the three-dimensional tomographic image was captured, radial pattern setting processing of setting a radial pattern with respect to the two-dimensional measurement region, image extraction processing of extracting a two-dimensional tomographic image in each of a plurality of lines of the radial pattern set in a redial pattern setting processing, and optic nerve head end detection processing of detecting a position of an end of the optic nerve head captured in the three-dimensional tomographic image.

Abstract: A target image to be corrected is generated by arranging partial images acquired by scanning a tissue of a living body with light and temporally continuously receiving the light from the tissue. A processor of a medical image processing device performs detecting position shift amounts, acquiring a component, and correcting. In the process of detecting position shift amounts, the processor detects the position shift amounts between the partial images (S3). In the process of acquiring, the processor acquires an assumed result of at least one of a component in the position shift amount caused by movement of the tissue, and a component in the position shift amount caused by a shape of the tissue (S4). In the process of correcting, the processor corrects a position of each of the partial images based on the component in the position shift amount (S7).

Abstract: An ophthalmic image processing apparatus includes a processor. The processor acquires a first image as a color fundus image, and corrects a pixel value of at least any color component in the first image, based on color gamut information for specifying a predetermined color gamut to be applied to a color fundus image, to generate a color gamut-corrected image.

Abstract: An ophthalmological image processing apparatus acquires a plurality of images of a subject eye photographed in a scanning-type imaging optical system, sets any one of the plurality of images as a template, sets corresponding points or corresponding regions between an image of the subject eye and the template at a plurality of positions of each of the image of the subject eye and the template, calculates a movement amount of each of the corresponding points or each of the corresponding regions, and corrects a distortion of the image of the subject eye with respect to the template based on the movement amount of each of the corresponding points or each of the corresponding regions.

Abstract: This invention stores necessary OCT data and reduces unnecessary OCT data. The OCT device includes: an OCT optical system that captures an image of a subject eye in accordance with a measurement action; a data obtaining section that obtains OCT data; a subject data determining section that determines non-reduction-subject data or reducing process section; a reducing process section that reduces a data amount of the reduction subject data or delete the reduction subject data; and a storage executing section that stores the non-reduction-subject data and after-reduction data in a storage section.

Abstract: A processor of an ophthalmologic image processing device acquires a plurality of B scan OCT data of a tissue of a subject eye, in which the plurality of B scan OCT data is captured at different scan positions, collectively sets an extraction region in the plurality of B scan OCT data to include an image of the subject eye in each of the plurality of B scan OCT data, and displays, on a predetermined display region, a plurality of extracted OCT data extracted from the respective B scan OCT data. Each of the plurality of extracted OCT data is switched to be alternatively displayed.

Abstract: A processor of a fundus image processing device acquires a fundus image 50 photographed by a fundus image photographing device. The processor acquires blood vessel images 60A and 60B that indicate at least one of an arteriole and a venule in the fundus image 50 by inputting the fundus image 50 into a mathematical model trained by a machine learning algorithm. The processor acquires a blood vessel area that is an area of at least one of the arteriole and the venule in the whole of the blood vessel images 60A and 60B.

Abstract: A control section of an ophthalmologic image processing apparatus acquires an ophthalmologic image captured by an ophthalmologic image capturing apparatus. The control section acquires, for the acquired ophthalmologic image, evaluation information indicating an appropriateness for acquiring medical data including at least any of an analysis result relating to a disease of a subject eye, an analysis result relating to the structure of the subject eye, and an image converted from the acquired ophthalmologic image. The control section acquires the medical data based on the acquired ophthalmologic image. The control section changes a medical data acquisition method according to whether or not the evaluation information on the ophthalmologic image satisfies a criterion.

Abstract: An image processing device processes an image of a subject eye. The image processing device includes an image acquisition unit that acquires an image of the subject eye, a diagnosis unit that obtains a diagnosis result of the subject eye based on the image acquired by the image acquisition unit, and a display control unit that changes a display mode of a display unit based on the diagnosis result.

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 ophthalmic image processing device processes an ophthalmic image of a subject eye. The ophthalmic image processing device includes a controller which acquires an ophthalmic image including a tomographic image of a plurality of tomographic planes in a subject eye, acquires a probability distribution for identifying two or more tissues included in a plurality of tissues in the tomographic image, by inputting the ophthalmic image into a mathematical model trained with using a machine learning algorithm, generates a structural abnormality degree map showing a two-dimensional distribution of a degree of abnormality of a structure in the tissue, for each of the two or more tissues, based on the probability distribution, and simultaneously displays two or more structural abnormality degree maps generated for each of the two or more tissues side by side on a display device.

Abstract: There are provided an ophthalmologic information analysis apparatus and an ophthalmologic information analysis program capable of efficiently performing follow-up observation of a subject eye. There is provided: a central processing unit configured to: acquire first analysis data obtained by analyzing an analysis region of the subject eye in first OCT data including the analysis region; acquire second OCT data captured on a day different from that of the first OCT data; perform an analysis comprising: specifying the analysis region corresponding to the first analysis data from the second OCT data; and acquiring second analysis data obtained by analyzing the analysis region in the second OCT data; and control a display to display the first analysis data and the second analysis data in a comparable manner.

Abstract: A processor of an ophthalmologic image processing device acquires intermediate information from which an influence of a position shift with respect to a first direction at each position in a second direction is excluded, for each of a first ophthalmologic image and a second ophthalmologic image (S4). The processor performs alignment with respect to the second direction between the first ophthalmologic image and the second ophthalmologic image, based on the intermediate information (S5, S6). The processor performs alignment with respect to the first direction between pixels at the same position with respect to the second direction in the first ophthalmologic image and the second ophthalmologic image for which the alignment with respect to the second direction has been performed (S7).