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.

Provided are an information processing apparatus, an information processing method, and a program capable of accumulating appropriate relearning data. An information processing apparatus includes an input unit that inputs input data to a learned model acquired in advance through machine learning using learning data, an acquisition unit that acquires output data output from the learned model through the input using the input unit, a reception unit that receives correction performed by a user for the output data acquired by the acquisition unit, and a storage controller that performs control for storing, as relearning data of the learned model, the input data and the output data that reflects the correction received by the reception unit in a storage unit in a case where a value indicating a correction amount acquired by performing the correction for the output data is equal to or greater than a threshold value.

A non-transitory computer-readable storage medium stores a control program that causes a computer to perform first frame displaying. In the first frame displaying, the computer displays thumbnails of a series of frames next to each other on a display, the series of frames constituting a dynamic image of a subject or constituting a dynamic analysis image obtained by analyzing the dynamic image.

The present invention relates to a medical data processing method of determining the representation of an anatomical body part (2) of a patient (1) in a sequence of medical images, the anatomical body part (2) being subject to a vital movement of the patient (1), the method being constituted to be executed by a computer and comprising the following steps: a) acquiring advance medical image data comprising a time-related advance medical image comprising a representation of the anatomical body part (2) in a specific movement phase; b) acquiring current medical image data describing a sequence of current medical images, wherein the sequence comprises a specific current medical image comprising a representation of the anatomical body part (2) in the specific movement phase, and a tracking current medical image which is different from the specific current medical image and comprises a representation of the anatomical body part (2) in a tracking movement phase which is different from the specific movement phase; c) determining, based on the advance medical image data and the current medical image data, specific image subset data describing a specific image subset of the specific current medical image, the specific image subset comprising the representation of the anatomical body part (2); d) determining, based on the current medical image data and the image subset data, subset tracking data describing a tracked image subset in the tracking current medical image, the tracked image subset comprising the representation of the anatomical body part (2).

A computer-implemented method for detecting lung abnormalities is provided. The method includes the steps of: acquiring a chest image of a subject; labeling one or more regions of interest (ROIs) in the chest image; segmenting a fine-grained boundary of each of the labeled ROIs; generating a plurality of output matrixes for each of the segmented ROIs; sorting a plurality of prediction scores obtained in the output matrixes, and generating a plurality of recommendations of potential abnormalities for each of the ROIs; and outputting the recommendations, A smart imagery framing and truthing (SIFT) system for implementing the method is also provided.

Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic system to assess pulmonary function and risk of mortality in patents with coronavirus disease 2019. The CAD system processes thoracic X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients. An alternative approach is to use an automatic CAD system to grade COVID-19 from computed tomography (CT) images to determine an accurate diagnosis of lung function.

A computing system includes a processor; and a memory having stored thereon an adjustment application comprising computer-executable instructions that, when executed, cause the computing system to: display a graphical user interface including a digital medical image of a patient; superimpose a bounding box; receive an adjustment of an area of interest; and provide an adjusted digital medical image. A non-transitory computer-readable medium includes computer-executable instructions that, when executed via one or more processors, cause a computer to: display a graphical user interface including a digital medical image of a patient; superimpose a bounding box; receive an adjustment of an area of interest; and provide an adjusted digital medical image. A computer-implemented method includes: displaying a graphical user interface including a digital medical image of a patient; superimposing a bounding box; receiving an adjustment of an area of interest; and providing an adjusted digital medical image.

A method of modeling lungs of a patient includes acquiring computed tomography data of a patient's lungs, storing a software application within a memory associated with a computer, the computer having a processor configured to execute the software application, executing the software application to differentiate tissue located within the patient's lung using the acquired CT data, generate a 3-D model of the patient's lungs based on the acquired CT data and the differentiated tissue, apply a material property to each tissue of the differentiated tissue within the generated 3-D model, generate a mesh of the 3-D model of the patient's lungs, calculate a displacement of the patient's lungs in a collapsed state based on the material property applied to the differentiated tissue and the generated mesh of the generated 3-D model, and display a collapsed lung model of the patient's lungs based on the calculated displacement of the patient's lungs.

This invention relates to an image processing device (1) comprising an input (2) for receiving image data representative of a region of interest in the body of a patient from a medical X-ray imaging apparatus (100). The image data comprises a first dark-field image obtained for a first X-ray spectrum and a second dark-field image obtained for a second, different, X-ray spectrum. A combination unit (3) provides a combination image that is representative of a medical condition map, e.g. a lung condition map, by combining the first dark-field image and the second dark-field image.

Using a computer-implemented intermediary by which contouring performed by two participants, such as two physicians, can be compared. First, contouring performed by each participant can be compared to contouring performed by the intermediary. Then, by way of the common intermediary and a transitive analysis, contouring performed by each participant can be compared.