Embodiments for assessing flow at an anatomical region of interest are disclosed. One embodiment uses pulsed contrast media injections at a known frequency along with corresponding image data to derive a measurement of blood flow velocity at the region of interest. Another embodiment uses incremental changes in known contrast media injection flow rates to match the blood flow rate relative to one of these known contrast media injection flow rates based on the presence of a particular indicia in image data. For example, this indicia can be the flow of contrast media out from a coronary artery back into the aorta or the onset of a steady state pixel density. A further embodiment uses contrast media injections that are synchronized with the cardiac cycle. For example, contrast media injections can be synchronized with the diastolic and/or systolic phases and used to measure blood flow accordingly.

The present disclosure provides a method for treating clinical or pre-clinical skin damage in a skin field of a subject, wherein the skin field has been allocated a skin cancerization field index (SCFI) score of at least 1 as determined by a process comprising the steps of: (i) assessing the number of keratoses in the skin field; (ii) assessing the thickness of the thickest keratosis in the skin field; and (iii) assessing the proportion of the field affected by clinical or subclinical skin damage. Based on the assessments made in (i), (ii) and (iii) the subject is optionally treated by at least one of (a) freezing one or more lesions, (b) shaving, curetting or surgically removing one or more lesions, (c) applying a topical treatment for actinic keratosis, basal cell carcinoma or squamous cell carcinoma, and (d) radiation therapy.

A medical image processing apparatus of an embodiment includes processing circuitry. The processing circuitry is configured to acquire time-series medical images including blood vessels of an examination subject, the time-series medical images being fluoroscopically captured in at least one direction at a plurality of points in time, generate a blood vessel shape model including time-series variation information about the blood vessels in an analysis region of the blood vessels on the basis of the acquired time-series medical images, and perform fluid analysis of blood flowing through the blood vessels on the basis of the generated blood vessel shape model.

A system performs cerebral hematoma analysis. The system includes a computing device receiving computerized tomography (CT) images from CT imaging devices. The CT images are associated with patients exhibiting cerebral hematomas. CT images may be converted into feature vectors and passed as input to a convolution neural network model for identification and diagnosis of hematoma volume changes. Detected changes may be thresholded to determine if the change represents an increase or shrinkage in the volumetry of the hematoma.

Systems and methods for rapid determination of microorganism growth and antimicrobial agent susceptibility and/or resistance are disclosed.

An ultrasound diagnostic apparatus 1 includes an image acquisition unit 3 that generates an ultrasound image, an image recognition unit 9 that performs image recognition for the ultrasound image to calculate recognition scores, an index value calculation unit 10 that calculates index values of a plurality of parts on the basis of the recognition scores calculated for a predetermined number of ultrasound images, a part narrowing-down unit 11 that narrows down target parts for which part determination is to be performed, from the plurality of parts on the basis of the index values, and a part determination unit 12 that determines an imaging part of the subject on the basis of the recognition scores calculated by the image recognition unit 9 for the target parts narrowed down by the part narrowing-down unit 11.

A method executed by a system for selecting images from a plurality of image groups originating from a plurality of imagers of an in-vivo device includes calculating, or otherwise associating, a general score (GS) for images of each image group, to indicate the probability that each image includes at least one pathology, dividing each image group into image subgroups, identifying a set Set(i) of maximum general scores (MGSs), a MGS for each image subgroup of each image group; and selecting images for processing by identifying a MGS|max in each set S(i) of MGSs; identifying the greatest MGS|max and selecting the image related to the greatest MGS|max. The method further includes modifying the set Set(i) of MGSs related to the selected image, and repeating the steps described above until a predetermined criterion selected from a group consisting of a number N of images and a score threshold is met.

Systems and methods for measuring changes in mechanical properties, mechanical responses, and or mechanical performances of orthodontic aligners are disclosed. The orthodontic aligners can include a plurality of speckles. The plurality of speckles are adapted for digital image correlation measurements. The methods involve acquiring digital images of the orthodontic aligner and performing digital image correlation on these images. From the digital image correlation, a change in one or more mechanical properties, mechanical responses, and/or mechanical performances in the orthodontic aligner are determined. The systems and methods can provide insight into mechanical properties of physiological structures, which can aid patient diagnosis and treatment.

Systems and methods for automatic assessment of a lesion are provided. One or more input medical images of a vessel of a patient is received. A lesion is defined in the one or more input medical images. A region of interest around the lesion is defined in the one or more input medical images. Radiomic features are extracted from the region of interest. An assessment of the lesion is determined using a machine learning based classifier network based on the radiomic features. The assessment of the lesion is output.

Systems and methods may be used for estimating instantaneous patient motion (a patient state). The patient state may be estimated based on a 3D reference volume and a stream of images, for example from an image acquisition device. The stream of images may be received in real-time, for example during a radiation therapy treatment. An example method may include encoding the 3D reference volume using a 3D encoder branch of a patient state generator network, encoding the stream of images using a 2D encoder branch of the patient state generator network, and combining the encoded 3D reference volume and the encoded real-time stream of images. The method may include estimating a 3D spatial transform that maps the 3D reference volume to a current patient state by decoding the combined encoding using a 3D decoder branch of the patient state generator network.