Abstract: A display control apparatus including at least one processor is provided. The processor is configured to display at least one first region including at least one finding on at least one first slice image of a first three-dimensional medical image on a display and display at least one second region with a maximum size of finding on at least one second slice image of at least one second three-dimensional medical image on the display.

Abstract: A lung region can be appropriately divided with a region dividing device, method, and program for dividing a lung region included in a medical image. A bronchus extracting unit 30 of a region dividing unit 22 extracts a bronchus from a lung region included in a medical image acquired by imaging a subject including a lung. A first dividing unit 31 to a fourth dividing unit 34 identify a plurality of branch positions of the bronchus and divide, based on the branch positions, the lung region into a plurality of regions.

Abstract: A finding classification unit classifies each pixel of a first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding. A similarity derivation unit derives a similarity between the first medical image and a second medical image on the basis of the first feature amount for each finding calculated in the first medical image and a second feature amount for each finding calculated in the second medical image. A region determination unit determines at least one important region including an important finding for deriving the similarity in the second medical image.

Abstract: A finding classification unit classifies each pixel of a first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding. A weighting coefficient setting unit sets a weighting coefficient indicating a degree of weighting, which varies depending on a size of each finding, for each finding. A similarity derivation unit performs a weighting operation for the first feature amount for each finding calculated in the first medical image and a second feature amount for each finding calculated in advance in a second medical image on the basis of the weighting coefficient to derive a similarity between the first medical image and the second medical image.

Abstract: A region division unit divides a target region of a first medical image into a plurality of regions. A finding classification unit classifies each pixel of the first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding. A region similarity derivation unit derives a region similarity between the first medical image and a second medical image for each of the divided regions. A similarity derivation unit performs a weighting operation for a plurality of region similarities with a weighting coefficient corresponding to at least one of a size of each of the divided regions or a size of a specific finding included in each of the divided regions to derive a similarity between the first medical image and the second medical image.

Abstract: A display control unit displays a tomographic image of a specific tomographic plane in a first medical image on a display unit. A finding classification unit classifies each pixel of a partial region of the first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding in the partial region. A weighting coefficient setting unit sets a weighting coefficient indicating a degree of weighting, which varies depending on a size of each finding, for each finding. A similarity derivation unit performs a weighting operation for the first feature amount for each finding calculated in the partial region and a second feature amount for each finding calculated in a second medical image on the basis of the weighting coefficient to derive a similarity between the first medical image and the second medical image.

Abstract: A learning assistance device acquires a plurality of learned discriminators obtained by causing learning discriminators provided in a plurality of respective terminal devices to perform learning using image correct answer data, acquires a plurality of discrimination results obtained by causing a plurality of learned discriminators to discriminate the same input image, determines the correct answer data of the input image on the basis of the plurality of discrimination results, causes the discriminator to perform learning the input image and the correct answer data, and outputs a result thereof as a new learning discriminator to each terminal device.

Abstract: A display control unit displays a tomographic image of a specific tomographic plane in a first medical image on a display unit. A finding classification unit classifies each pixel of a partial region of the first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding in the partial region. A weighting coefficient setting unit sets a weighting coefficient indicating a degree of weighting, which varies depending on a size of each finding, for each finding. A similarity derivation unit performs a weighting operation for the first feature amount for each finding calculated in the partial region and a second feature amount for each finding calculated in a second medical image on the basis of the weighting coefficient to derive a similarity between the first medical image and the second medical image.

Abstract: A finding classification unit classifies each pixel of a first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding. A similarity derivation unit derives a similarity between the first medical image and a second medical image on the basis of the first feature amount for each finding calculated in the first medical image and a second feature amount for each finding calculated in the second medical image. A region determination unit determines at least one important region including an important finding for deriving the similarity in the second medical image.

Abstract: A display control unit displays a tomographic image of a specific tomographic plane in a first medical image on a display unit. A finding classification unit classifies each pixel of a partial region of the first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding in the partial region. A weighting coefficient setting unit sets a weighting coefficient indicating a degree of weighting, which varies depending on a size of each finding, for each finding. A similarity derivation unit performs a weighting operation for the first feature amount for each finding calculated in the partial region and a second feature amount for each finding calculated in a second medical image on the basis of the weighting coefficient to derive a similarity between the first medical image and the second medical image.

Abstract: A similarity determination apparatus comprising a processor configured to: classify each pixel of a first medical image into at least one of a plurality of types of findings; calculate a first feature amount for each classified finding; set a weighting coefficient indicating a degree of weighting which varies depending on a size of each finding for each classified finding; derive an adjusted weighting coefficient by adjusting the weighting coefficient for each of a plurality of finding groups, into which the plurality of types of findings are classified; and derive the similarity between the first medical image and a second medical image by performing a weighting operation for the first feature amount for each finding in the first medical image and a second feature amount for each finding calculated in advance in the second medical image, for each of the finding groups on the basis of the adjusted weighting coefficient.

Abstract: An acquisition unit 47 acquires comparative cases, which are medical examination data having the same clinical path as a medical examination target patient, from an electronic medical record DB 14. A display form determination unit 48 compares the treatment periods and the conditions of respective comparative cases, and assigns each comparative case to each group. A display color is set in advance for each group. A screen display control unit 49 generates a graph comparison display screen 21 on which a line graph 70 showing a time-series change in the examination value of the medical examination target patient and a plurality of line graphs 71 showing time-series changes in the examination values of respective comparative cases are displayed so as to overlap each other. The line graph 71 is color-coded with display colors determined by the display form determination unit 48.

Abstract: A lung region can be appropriately divided with a region dividing device, method, and program for dividing a lung region included in a medical image. A bronchus extracting unit 30 of a region dividing unit 22 extracts a bronchus from a lung region included in a medical image acquired by imaging a subject including a lung. A first dividing unit 31 to a fourth dividing unit 34 identify a plurality of branch positions of the bronchus and divide, based on the branch positions, the lung region into a plurality of regions.

Abstract: A finding classification unit classifies each pixel of a first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding. A weighting coefficient setting unit sets a weighting coefficient indicating a degree of weighting, which varies depending on a size of each finding, for each finding. A similarity derivation unit performs a weighting operation for the first feature amount for each finding calculated in the first medical image and a second feature amount for each finding calculated in advance in a second medical image on the basis of the weighting coefficient to derive a similarity between the first medical image and the second medical image.

Abstract: A region division unit divides a target region of a first medical image into a plurality of regions. A finding classification unit classifies each pixel of the first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding. A region similarity derivation unit derives a region similarity between the first medical image and a second medical image for each of the divided regions. A similarity derivation unit performs a weighting operation for a plurality of region similarities with a weighting coefficient corresponding to at least one of a size of each of the divided regions or a size of a specific finding included in each of the divided regions to derive a similarity between the first medical image and the second medical image.

Abstract: A learning assistance device acquires a plurality of learned discriminators obtained by causing learning discriminators provided in a plurality of respective terminal devices to perform learning using image correct answer data, acquires a plurality of discrimination results obtained by causing a plurality of learned discriminators to discriminate the same input image, determines the correct answer data of the input image on the basis of the plurality of discrimination results, causes the discriminator to perform learning the input image and the correct answer data, and outputs a result thereof as a new learning discriminator to each terminal device.

Abstract: A weighting processing unit performs a weighting process for an individual lesion similarity which is a similarity between a target lesion and a case lesion of the same type, using a first weighting coefficient corresponding to the degree of contribution of each type of lesion to the specification of a disease name, to calculate a processed individual lesion similarity corresponding to a first purpose of specifying the disease name. In addition, the weighting processing unit performs the weighting process for the individual lesion similarity, using a second weighting coefficient corresponding to the degree of contribution of each type of lesion to the specification of a severity, to calculate a processed individual lesion similarity corresponding to a second purpose of specifying the severity. A total similarity between a target image and a case image is calculated for each purpose on the basis of the processed individual lesion similarities.

Abstract: A learning assistance device acquires a plurality of learned discriminators obtained by causing learning discriminators provided in a plurality of respective terminal devices to perform learning using image correct answer data, acquires a plurality of discrimination results obtained by causing a plurality of learned discriminators to discriminate the same input image, determines the correct answer data of the input image on the basis of the plurality of discrimination results, causes the discriminator to perform learning the input image and the correct answer data, and outputs a result thereof as a new learning discriminator to each terminal device.

Abstract: A medical test result display device and an operating method thereof are provided. An integration server reads out medical information from a medical case DB on the basis of a patient ID of a delivery request. A screen producing section reads out important test items from important test item information in accordance with an injury/disease code in the delivery request, and extracts test values on the important test items from the medical information. A case list creating section reads out representative case information from the medical case DB on the basis of the injury/disease code. The screen producing section produces a comparative display screen showing a test value graph based on the test values extracted from the medical information and a representative case graph based on test values in the representative case information in a manner allowing comparison therebetween, and delivers the screen to a department terminal to display same.

Abstract: A first classification unit outputs a plurality of evaluation values indicating the possibility of being each of a plurality of types of case regions for each pixel of a three-dimensional image. Based on selected conversion definition information, a second classification unit converts a first classification result for each pixel of the three-dimensional image based on the plurality of evaluation values, and outputs a second classification result for each pixel of the three-dimensional image.