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: 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 seal member includes a ?? precipitation-hardening alloy, in which the ?? precipitation-hardening alloy has a component composition of, in mass %: Ni: from 40 to 62%; Cr: from 13 to 20%; Ti: from 1.5 to 2.8%; Al: from 1.0 to 2.0% (provided that Ti/Al: 2.0 or less); Nb: 2.0% or less; Ta: 2.0% or less (provided that Nb+Ta: from 0.2 to 2.0%); B: from 0.001 to 0.010%; W: 3.0% or less; and Mo: 2.0% or less (provided that Mo+(1/2)W: from 1.0 to 2.5%), and optionally, C: 0.08% or less; Si: 1.0% or less; Mn: 1.0% or less; P: 0.02% or less; and S: 0.01% or less, with the balance being Fe and inevitable impurities, and in which the seal member has a hardness of 250 Hv or more, and includes a cold-rolled microstructure obtained by a cold rolling.

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: The present invention relates to a Co-based alloy product including a polycrystal of a Co-based alloy, the Co-based alloy including: 0.001 mass %?C<0.100 mass %; 9.0 mass %?Cr<20.0 mass %; 2.0 mass %?Al<5.0 mass %; 13.0 mass %?W<20.0 mass %; and 39.0 mass %?Ni<55.0 mass %, with the balance being Co and unavoidable impurities, in which the Co-based alloy product comprises segregated cells formed inside a crystal grain of the polycrystal, the segregated cells have an average size of 1 ?m or larger and 100 ?m or smaller, and the segregated cells contain Al and Cr, and a method for producing the Co-based alloy product.

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: 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: 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).

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 ocular fundus image processing apparatus acquires a fundus image of an subject eye which is captured by a fundus capturing apparatus, acquires a correction image including artifact caused by illumination light without including a fundus of the subject eye as a capturing target, executes conversion processing of the correction image a plurality of times while changing processing content to acquire a plurality of conversion images, and acquires a difference image, as high-quality images, in which an influence of the artifact is suppressed to be less than or equal to a criterion, among a plurality of difference images acquired by taking a difference between each of the plurality of conversion images and the fundus 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: 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: 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.