Abstract: A document creation support apparatus acquires information indicating a plurality of regions of interest included in a medical image, derives an evaluation index as a target of a medical document for each of the plurality of regions of interest, and generates text including a description regarding at least one of the plurality of regions of interest based on the evaluation index.

Abstract: A processor acquires a three-dimensional image of a subject, acquires a radiation image of the subject having a lumen structure into which an endoscope is inserted, acquires a first real endoscopic image in the lumen structure of the subject captured at a first time point by the endoscope, derives a provisional virtual viewpoint in the three-dimensional image of the endoscope using the radiation image and the three-dimensional image, derives a virtual viewpoint at the first time point in the three-dimensional image of the endoscope using the provisional virtual viewpoint, the first real endoscopic image, and the three-dimensional image, and derives a virtual viewpoint at a second time point after the first time point in the three-dimensional image of the endoscope using the first real endoscopic image and a second real endoscopic image captured by the endoscope at the second time point.

Abstract: A processor acquires a three-dimensional image of a subject, acquires a real endoscopic image in a lumen structure of the subject, which is picked up by an endoscope inserted into the lumen structure of the subject, derives a virtual viewpoint in the three-dimensional image of the endoscope using the real endoscopic image and the three-dimensional image, acquires a radiation image of the subject in which the endoscope is inserted into the lumen structure, performs registration between the radiation image and the three-dimensional image, and superimposes and displays at least a part of the lumen structure included in the three-dimensional image, on the radiation image, based on the virtual viewpoint and a result of the registration.

Abstract: A processor acquires a medical image and a first disease region in the medical image, derives a second disease region related to the first disease region in the medical image based on the medical image and the first disease region, updates the first disease region based on the medical image and the second disease region, updates the second disease region based on the medical image and the updated first disease region, and repeats update of the first disease region and update of the second disease region until a predetermined end condition is satisfied.

Abstract: Provided are an image processing method, an image processing device, a program, and a trained model that can suppress calculation resources required for registration between a plurality of images. An image processing method that is executed by one or more processors includes: allowing the one or more processors to acquire feature maps of each of a plurality of images; and allowing the one or more processors to calculate a deformation vector field from a combination of the feature maps of each image. The one or more processors generate the feature maps respectively from the plurality of images using a first neural network and input the generated combination of the feature maps to a second neural network to calculate the deformation vector field.

Abstract: Provided are a disease region extraction apparatus, a disease region extraction method, and a disease region extraction program that can extract an infarction region even in an image in which it is difficult to prepare a large amount of data indicating a correct infarction region. A disease region extraction apparatus includes: an image acquisition unit that acquires a first image obtained by capturing an image of a subject that has developed a disease; an estimated image derivation unit that estimates a second image, whose type is different from the type of the first image, from the first image to derive an estimated image; and a disease region extraction unit that extracts a disease region from the estimated image.

Abstract: An image alignment apparatus includes at least one processor, and the processor derives, for each of first and second three-dimensional images each including a plurality of tomographic images and a common structure, first and second three-dimensional coordinate information that define an end part of the structure in a direction intersecting the tomographic image. The processor aligns the first three-dimensional image and the second three-dimensional image by using the first and second three-dimensional coordinate information to align the common structure included in each of the first three-dimensional image and the second three-dimensional image at least in the direction intersecting the tomographic image.

Abstract: An image processing apparatus includes at least one processor, and the processor derives three-dimensional coordinate information that defines a position of a structure in a tomographic plane from a tomographic image including the structure, and that defines a position of an end part of the structure outside the tomographic plane in a direction intersecting the tomographic image.

Abstract: A learning image generation device includes an image acquisition unit that acquires a learning image, and a variation learning image generation unit that generates a variation learning image by adding a variation in which an output of a model deviates from a target value to a pixel value of at least one pixel that constitutes the learning image in a case in which the learning image acquired by the image acquisition unit is input to the model.

Abstract: An analysis result acquisition unit acquires an analysis result of a medical image. A medical information acquisition unit acquires medical information different from the medical image. A priority deriving unit derives a priority of the medical image based on the analysis result and the medical information.

Abstract: A learning image generation device including: a supervised data acquisition unit that acquires supervised data including a learning image and a correct learning image in which a correct region is defined in the learning image as a pair; and a variation learning image generation unit that generates a variation learning image in which a pixel value of a pixel belonging to the correct region is varied within a limitation range of an allowable pixel value of the pixel belonging to the correct region in the learning image

Abstract: A discriminator includes a common learning unit and a plurality of learning units that are connected to an output unit of the common learning unit. The discriminator is trained, using a plurality of data sets of a medical image and an image data of a first disease region, such that information indicating the first disease region is output from a first learning unit in a case in which the medical image is input to the common learning unit. The discriminator is trained, using a plurality of data sets of a medical image and an image data of a second disease region having at least one of a medical causal relationship or an anatomic causal relationship with the first disease, such that information indicating the second disease region is output from a second learning unit in a case in which the medical image is input to the common learning unit.

Abstract: A discriminator includes a common learning unit and a plurality of learning units that are connected to an output unit of the common learning unit. The discriminator is trained, using a plurality of data sets of a first image obtained by capturing an image of a subject that has developed a disease and an image data of a disease region in the first image, such that information indicating the disease region is output from a first learning unit in a case in which the first image is input to the common learning unit. In addition, the discriminator is trained, using a plurality of data sets of an image set obtained by registration between the first image and a second image whose type is different from the type of the first image, such that an estimated image of the second image is output from an output unit of a second learning unit.

Abstract: Reception means receives examination data including a plurality of examination images. Association means associates an examination image similar to each sample image included in the layout with a sample image using a similarity between each sample image included in a layout and each of a plurality of examination images included in examination data. Display means displays the examination image associated with the sample image at an arrangement position where the sample image is arranged in the layout.

Abstract: An image diagnosis support apparatus includes: an acquisition unit that acquires a plurality of first image data groups and a plurality of second image data groups to be subjected to comparative interpretation; an association unit that associates each of the plurality of first image data groups and each of the plurality of second image data groups with each other based on a degree of similarity between the image data groups; an image extraction unit that extracts a corresponding image corresponding to at least one target image of the first image data group from a second image data group among the plurality of second image data groups associated with a first image data group among the plurality of first image data groups; and a display controller that displays a set of images of the target image and the corresponding image on a display unit in a contrastable layout.

Abstract: Similarity acquisition means calculates a similarity in each combination of an examination cross-sectional image and a reference cross-sectional image between examination volume data and reference volume data. Adjustment value acquisition means acquires an adjustment value of the similarity based on a relationship between the cross-sectional positions of examination cross-sectional images in two combinations and a relationship between the cross-sectional positions of reference cross-sectional images in the two combinations. Association means associates the examination cross-sectional image and the reference cross-sectional image with each other based on a sum of all the similarities and all the adjustment values.

Abstract: An analysis result acquisition unit acquires an analysis result of a medical image. A medical information acquisition unit acquires medical information different from the medical image. A priority deriving unit derives a priority of the medical image based on the analysis result and the medical information.

Abstract: A discriminator includes a common learning unit and a plurality of learning units that are connected to an output unit of the common learning unit. The discriminator is trained, using a plurality of data sets of a medical image and an image data of a first disease region, such that information indicating the first disease region is output from a first learning unit in a case in which the medical image is input to the common learning unit. The discriminator is trained, using a plurality of data sets of a medical image and an image data of a second disease region having at least one of a medical causal relationship or an anatomic causal relationship with the first disease, such that information indicating the second disease region is output from a second learning unit in a case in which the medical image is input to the common learning unit.

Abstract: A discriminator includes a common learning unit and a plurality of learning units that are connected to an output unit of the common learning unit. The discriminator is trained, using a plurality of data sets of a first image obtained by capturing an image of a subject that has developed a disease and an image data of a disease region in the first image, such that information indicating the disease region is output from a first learning unit in a case in which the first image is input to the common learning unit. In addition, the discriminator is trained, using a plurality of data sets of an image set obtained by registration between the first image and a second image whose type is different from the type of the first image, such that an estimated image of the second image is output from an output unit of a second learning unit.

Abstract: Provided are a disease region extraction apparatus, a disease region extraction method, and a disease region extraction program that can extract an infarction region even in an image in which it is difficult to prepare a large amount of data indicating a correct infarction region. A disease region extraction apparatus includes: an image acquisition unit that acquires a first image obtained by capturing an image of a subject that has developed a disease; an estimated image derivation unit that estimates a second image, whose type is different from the type of the first image, from the first image to derive an estimated image; and a disease region extraction unit that extracts a disease region from the estimated image.