This invention concerns an automatic segmentation method of features, such as anatomical and pathological structures or instruments, which are visible in a 3D medical image of a subject, composed of voxels. Said method being characterised in that it consists in providing a global software means or arrangement combining N different convolutional neural networks or CNNs, with N≥2, and having a structured geometry or architecture adapted and comparable to that of the image volume, and in analysing voxels forming said volume of the 3D image according to N different reconstruction axes or planes, each CNN being allocated to the analysis of the voxels belonging to one axis or plane.

The present disclosure relates to methods and systems for vascular image processing. The method may include obtaining an initial vascular image, generating a vascular fragment image by performing a vascular fragmentation operation on the initial vascular image, and generating, based on the vascular fragment image, a vascular centerline image.

A computer system is provided for processing ultrasonic data of an ultrasonic probe applied to an area of an aircraft component that includes carbon fiber reinforced polymer. C-scan data is obtained and a preliminary mesh is defined over the C-scan data by taking into account the underlying structural or mechanical characteristics of the analyzed component. The mesh is further refined and data gathered for each mesh cell. A heat map is generated based on the mesh.

A method and system for selecting an intraocular lens, with a controller having a processor and tangible, non-transitory memory. A plurality of machine learning models is selectively executable by the controller. The controller is configured to receive at least one pre-operative image of the eye and extract, via a first input machine learning model, a first set of data. The controller is configured to receive multiple biometric parameters of the eye and extract, via a second input machine learning model, a second set of data. The first set of data and the second set of data are combined to produce a mixed set of data. The controller is configured to generate, via an output machine learning model, at least one output factor based on the mixed set of data. An intraocular lens is selected based in part on the at least one output factor.

Provided in the present application is an ultrasonic imaging method, including: generating a plurality of anatomical plane schematics, and causing a display to display the same, each one of the plurality of anatomical plane schematics respectively corresponding to a different anatomical plane of interest; acquiring an ultrasonic image of the anatomical plane of interest; and automatically generating an ultrasonic image thumbnail of the anatomical plane of interest, automatically replacing the anatomical plane schematic corresponding to the anatomical plane of interest with the ultrasonic image thumbnail, and causing the display to display the same. Also provided in the present application are an ultrasonic imaging system and a non-transitory computer-readable medium.

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

The ultrasonic diagnostic apparatus according to the present embodiment includes a frequency characteristic analysis circuit, a filter setting circuit, and a filter processing circuit. The frequency characteristic analysis circuit performs a frequency analysis on a first reception signal corresponding to a region of interest of each depth, and acquires a frequency characteristic of each depth. The filter setting circuit sets a reception filter of each depth based on the acquired frequency characteristic of each depth such that the acquired frequency characteristic of each depth shows a predetermined frequency characteristic. The filter processing circuit applies the set reception filter of each depth to a second reception signal corresponding to the region of interest of each depth, the second reception signal being after the first reception signal, and converts the second reception signal into a third reception signal corresponding to the region of interest of each depth.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

An object volume acquisition method of an ultrasonic image, for a probe of an ultrasonic system is disclosed. The volume acquisition method of the object in the ultrasonic image includes collecting, by the probe, a plurality of two-dimensional ultrasonic images; obtaining the plurality of two-dimensional ultrasonic images, an offset angle, a rotation axis and a frequency of the probe corresponding to the plurality of two-dimensional ultrasonic images; segmenting a first image including an ultrasonic image object from each two-dimensional ultrasonic image of the plurality of two-dimensional ultrasonic images based on a deep learning structure; determining a contour of the ultrasonic image object; reconstructing a three-dimensional model corresponding to the ultrasonic image object according to the contour of the ultrasonic image object corresponding to the each two-dimensional ultrasonic image; and calculating a volume of the ultrasonic image object according to the three-dimensional model corresponding to the ultrasonic image object.

Aspects of the disclosure provide for methods, systems, and apparatuses, including computer-readable storage media, for lipid detection by identifying fibrotic caps in medical images of blood vessels. A method includes receiving one or more input images of a blood vessel and processing the one or more input images using a machine learning model trained to identify locations of fibrotic caps in blood vessels. The machine learning model is trained using a plurality of training images each annotated with locations of one or more fibrotic caps. A method includes identifying and characterizing fibrotic caps of lipid pools based on differences in radial signal intensities measured at different locations of an input image. A system can generate one or more output images having segments that are visually annotated representing predicted locations of fibrotic caps covering lipidic plaques.