Disclosed are a method for displaying ultrasonic data and an ultrasound imaging system. The method may include: acquiring ultrasonic video data to be displayed; obtaining at least one representative frame from the ultrasonic video data; classifying the representative frame to obtain a category of the representative frame, and determining a category of the ultrasonic video data according to the category of the representative frame; and displaying in categories the ultrasonic video data according to the category of the ultrasonic video data.

Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images. In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) to detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject. The machine learning modules may be used not only to detect presence and locations of such regions within an image, but also to segment the region corresponding to the lesion and/or classify such hotspots based on the likelihood that they are indicative of a true, underlying cancerous lesion. This AI-based lesion detection, segmentation, and classification can provide a basis for further characterization of lesions, overall tumor burden, and estimation of disease severity and risk.

The present disclosure is directed towards systems and methods for detecting and sizing mineralized tissue. An exemplary method, according to an embodiment of the present disclosure, can provide for imaging a region of interest containing the mineralized tissue with unfocused ultrasound beams via a primary imaging method. The method can then provide for computing a wavefront coherence at the imaged region of interest. The method can then provide for segmenting pixels of the imaged region of interest based on their intensities and intensities of surrounding pixels. The method can then provide for identifying a border and a shadow of the mineralized tissue based on the segmenting. Then, the method can provide for calculating a size of the mineralized tissue based on the border and the shadow.

A method of mapping autoregulation of a kidney using magnetic resonance (MR) imaging is provided. The method includes acquiring a time series of MR images of a subject while the subject is at rest by acquiring MR images of an imaging region repeatedly over a period of time by applying a pulse sequence. The imaging region includes at least a kidney of the subject. The method also includes analyzing the time series of MR images along a temporal dimension, and identifying features associated with autoregulation of the kidney including tubuloglomerular feedback (TGF) in voxels of the MR images based on the analysis. The method further includes generating a map and/or a metric of the autoregulation based on the identified features, and outputting the map and/or the metric.

An ultrasound diagnostic apparatus 1 includes a bladder pattern storage unit 22, a reference pattern setting unit 21, a bladder extraction unit 18 that extracts a bladder region from an ultrasound image, a bladder extraction success/failure determination unit 19 that determines whether the bladder region represents a bladder having the reference pattern, and an image quality adjustment unit 20 that adjusts the image quality of the ultrasound image in a case where determination is made that the bladder region does not represent the bladder having the reference pattern, in which in a case where the determination is made that the bladder region does not represent the bladder having the reference pattern even in an ultrasound image of which the image quality is adjusted, the bladder extraction success/failure determination unit 19 determines whether the bladder region represents the bladder having the abnormal bladder pattern.

An image segmentation model training method includes acquiring a first image, a second image, and a labeled image of the first image; acquiring a first predicted image according to a first network model; acquiring a second predicted image according to a second network model; determining a reference image of the second image based on the second image and the labeled image of the first image; and updating a model parameter of the first network model based on the first predicted image, the labeled image, the second predicted image, and the reference image to obtain an image segmentation model.

A time phase determination apparatus according to an embodiment is a time phase determination apparatus for determining the range of a particular time phase in a contrast-enhanced image, and includes processing circuitry. The processing circuitry acquires medical images at a plurality of different timings. The processing circuitry extracts a plurality of regions of interest on the basis of the medical images at the different timings. The processing circuitry generates a plurality of time intensity curves that are time intensity curves corresponding to the respective regions of interest. The processing circuitry determines the range of the particular time phase on the basis of the time intensity curves.

The present invention relates to a method for providing urinary stone information, and more particularly, to a method for providing urinary stone information, capable of providing information necessary for urinary stone surgery by detecting a region where a stone is present from a plurality of tomography images by using a machine learning model, and automatically extracting information including a location and a size of the stone.

Methods for training an algorithm to identify structural anatomical features, for example of a blood vessel, in a non-contrast computed tomography (NCT) image are described herein. The algorithm may comprise an image segmentation algorithm, a random forest classifier, or a generative adversarial network in examples described herein. In one embodiment, a method comprises receiving a labelled training set for a machine learning image segmentation algorithm. The labelled training set comprising a plurality of NCT images, each NCT image of the plurality of NCT images showing a targeted region of a subject, the targeted region including at least one blood vessel. The labelled training set further comprises a corresponding plurality of segmentation masks, each segmentation mask labelling at least one structural feature of a blood vessel in a corresponding NCT image of the plurality of NCT images. The method further comprises training a machine learning image segmentation algorithm, using the plurality of NCT images and the corresponding plurality of segmentation masks, to learn features of the NCT images that correspond to structural features of the blood vessels labelled in the segmentation masks, and output a trained image segmentation model. The method further comprises outputting the trained image segmentation model usable for identifying structural features of a blood vessel in an NCT image. Further methods are described herein for identifying anatomical features from an NCT image, and for establishing training sets. Computing apparatuses and computer readable media are also described herein.

A system for image segmentation is provided. The system may obtain a target image including an ROI, and segment a preliminary region representative of the ROI from the target image using a first ROI segmentation model corresponding to a first image resolution. The system may segment a target region representative of the ROI from the preliminary region using a second ROI segmentation model corresponding to a second image resolution. At least one model of the first and second ROI segmentation models may at least include a first convolutional layer and a second convolutional layer downstream to the first convolutional layer. A count of input channels of the first convolutional layer may be greater than a count of output channels of the first convolutional layer, and a count of input channels of the second convolutional layer may be smaller than a count of output channels of the second convolutional layer.