Devices, systems, computer program products and computer implemented methods are provided for dynamically assessing a moving object from a sequence of consecutive volumetric image frames of such object, which images are timely separated by a certain time interval, by: identifying in at least one image of the sequence the object of interest; segmenting the object to identify object contour; propagating the object contour as identified to other images of the sequence; and performing dynamic analysis of the object based on the object contour as propagated.

According to an embodiment, a method for generating a digital three-dimensional model representing development in dental condition for a tooth is disclosed. The method includes obtaining, at different timepoints, a first digital 3D model of a patient's set of teeth including first texture data and a second digital 3D model of the patient's set of teeth including second texture data. The first digital 3D model including the first texture data and second digital 3D model including the second texture data are placed in a common texture space by uniformizing texture. Lastly, the digital three-dimensional model representing development in dental condition is generated based on a comparison of the first texture data and the second texture data of corresponding regions in the first digital 3D model and the second digital 3D model placed in the common texture space.

Provided is a technique for preventing erroneous recognition of a fine particle region from a captured image of fine particles. A fine particle testing apparatus of the present disclosure includes: an imaging part capturing a first fine particle image of a well that holds a liquid containing fine particles; an image processor executing a process of generating a second fine particle image by extracting a contour of the first fine particle image, a process of performing a logical operation between the first fine particle image and the second fine particle image, a process of calculating a feature amount of the fine particles based on a result of the logical operation, and a process of determining growth of the fine particles in the well based on the calculated feature amount; and an output part outputting a result of the determination.

The acquisition unit 41B acquires a target image indicating a target object of inspection. The detection unit 42B detects, on the basis of a group of images each of which indicates the target object in a normal state, the target image that indicates the target object that is not in the normal state among the target images that the acquisition unit 41B acquires.

A method for providing a lung disease risk measure in a Computer Aided Diagnosis system is described. The method comprising the steps of: receiving a plurality of inputs for a subject, each input comprising at least one image showing all or part of the lungs of a patient and a time stamp for the image, where the inputs are obtained at varying intervals; analysing the inputs to assess temporal changes in the images using at least one of an input data encoder and a time stamp encoder; inputting the output of at least one of the encoders to a score calculator to calculate a risk score; outputting the risk score indicating the lung disease risk for the subject. A Computer Aided diagnosis system for implementing the method is also described.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.

A system and method of using machine learning to predict the pharmacokinetics of a therapeutic radiopharmaceutical on a subject patient using the biodistribution data of the patient in order to dynamically treat the patient using the radiopharmaceutical.

A method is for the characterization of plaque in a region of interest inside an examination subject by way of a plurality of image data sets. The image data sets have been reconstructed from a plurality of projection data sets, which have been acquired via a CT device using different X-ray energy spectra. The method includes: acquiring the image data sets, which include a plurality of pixels. Spectral parameter values are acquired on a pixel by pixel basis using at least two image data sets. Character parameter values are then acquired on a pixel by pixel basis to characterize plaques on the basis of the spectral parameter values. An analysis unit and a computed tomography system are also disclosed.

A method for processing angiography image data by using an imaging catheter path that is directly detected from the angiography data as a co-registration path or using detected marker locations from the angiography data to generate a co-registration path. If the acquired angiography data includes synchronized cardiac phase signals and a predetermined quantity of angiography image frames not including contrast media, then a directly detected imaging catheter path is used as the co-registration path. Otherwise the co-registration path is determined based upon detected marker locations from the angiography image data.

One or more example embodiments describes a method of performing lung nodule assessment, which method comprises the steps of obtaining a lung scan for a patient from an imaging modality; obtaining a blood panel for that patient from a blood analysis modality; and processing the lung scan and the blood panel in a classifier, which classifier is trained to assess a lung nodule based on the lung scan and the blood panel. The invention further describes a method of training such a classifier, and a lung nodule assessment arrangement.