Provided is a dynamic analysis apparatus that predicts a respiratory function value based on frame images showing dynamics of chest. The dynamic analysis apparatus includes a hardware processor that obtains a first lung size value of a removal target site and a second lung size value of a left or right lung field including the removal target site, calculates a proportion between the first and second lung size values as a size proportion, calculates a first feature amount concerning respiratory function of the left or right lung field including the removal target site and a second feature amount concerning respiratory function of the lung fields as a whole, calculates a proportion between the first and second feature amounts as a feature amount proportion, and calculates an expected rate of the respiratory function without the removal target site, based on a product of the size proportion and the feature amount proportion.

A computer-executed method implementing a deep learning technique is carried out to perform on canine thoracic radiographic images an automated diagnosis of left atrial enlargement as an early sign of myxomatous mitral valve insufficiency.

An object detection device that detects a specific object included in an input image includes a first candidate region specifying unit that specifies a first candidate region in which an object candidate is included from a first input image obtained by imaging a subject in a first posture, a second candidate region specifying unit that specifies a second candidate region in which an object candidate is included from a second input image obtained by imaging the subject in a second posture different from the first posture, a deformation displacement field generation unit that generates a deformation displacement field between the first input image and the second input image, a coordinate transformation unit that transforms a coordinate of the second candidate region to a coordinate of the first posture based on the deformation displacement field, an association unit that associates the first candidate region with the transformed second candidate region that is close to the first candidate region, and a same object determination unit that determines that the object candidates included in the candidate regions associated with each other by the association unit are the same object and are the specific object.

This disclosure relates to detecting visual findings in anatomical images. Methods comprise inputting anatomical images into a neural network to output a feature vector and computing an indication of visual findings being present in the images by a dense layer of the neural network that takes as input the feature vector and outputs an indication of whether each of the visual findings is present in the anatomical images. The neural network is trained on a training dataset including anatomical images, and labels associated with the anatomical images and each of the visual findings. The visual findings may be organised as a hierarchical ontology tree. The neural network may be trained by evaluating the performance of neural networks in detecting the visual findings and a negation pair class which comprises anatomical images where a first visual finding is identified in the absence of a second visual finding.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain contrast-enhanced images related to an examined subject and corresponding to a plurality of temporal phases. On the basis of data values of pixels in the contrast-enhanced images corresponding to the plurality of temporal phases, the processing circuitry is configured to determine which one of contrast enhancement dominance and fluid accumulation dominance corresponds to the pixels or a region including the pixels. The processing circuitry is configured to generate a display mode based on the determination.

An effect of a treatment on an organ, e.g., a lung, is assessed by acquiring a first measurement for each of a plurality of regions of the organ, and then acquiring a second measurement for each of the plurality of regions of the organ, after acquisition of the first measurements. A regional change measurement is obtained for each of the plurality of regions of the organ based on the first measurement and the second measurement of the region. A treatment effect is then determined based the plurality of regional change measurements and treatment information of the treatment delivered to the organ.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain a medical image. The processing circuitry is configured to calculate a first blood flow direction on the basis of a structure of a region of interest rendered in the medical image. The processing circuitry is configured to calculate a second blood flow direction on the basis of a structure in the surroundings of the region of interest. The processing circuitry is configured to identify a condition of the region of interest, on the basis of the first blood flow direction and the second blood flow direction.

The present invention provides a severity evaluation apparatus that enables optimum distribution of medical resources to provide optimized medical care. A model generation apparatus 10 has a teaching data acquisition unit 31 operable to acquire teaching data 90 labeled with disease information indicative of a level of disease of a body region for a CT image and a model generation unit 32 operable to generate a disease information model 50 configured to output disease information in response to an input of a CT image with machine learning that uses the teaching data 90. The severity evaluation apparatus 20 has a measurement data acquisition unit 41 operable to acquire CT images 60 of a patient, a disease information acquisition unit 42 operable to acquire disease information 70 from each of the CT images 60 with use of the disease information model 50, and a severity calculation unit 43 operable to calculate a severity 75 of the patient based on the disease information 70 acquired by the disease information acquisition unit 42.

In one aspect of the invention a system and method is claimed for providing model parameters for three dimensional fabrication of anatomical structures by obtaining and reconstructing three dimensional image data with a medical imager wherein imaging acquisition parameters of the imaging system and/or reconstruction input parameters of the reconstructor are optimized for maximum geometry precision. Advantageously, the imaging system is further configured to obtain material and/or functional information of the anatomical structure model and that material information is used to incorporate the material information in the anatomical model.

A processor generates a structure-highlighted synthesized two-dimensional image from a plurality of tomographic images and detects a structure of interest from the plurality of tomographic images and the structure-highlighted synthesized two-dimensional image. The processor sets at least some of the plurality of tomographic images as storage-required images or non-storage-required images according to a result of comparison between the structure of interest detected from the plurality of tomographic images and the structure of interest detected from the structure-highlighted synthesized two-dimensional image.