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.

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.

Techniques for detecting depth contours of an anatomical target and for enhancing imaging of the anatomical target are provided. In an example, a reference pattern of light can be projected across an anatomical target and an image of the reflected light pattern upon the anatomical target can be captured. The captured light pattern can be analyzed to determine contour information, which can then be used to provide 3D cues to enhance a 2-dimensional image of the anatomical target.

Presented herein are systems and methods that provide for improved 3D segmentation of nuclear medicine images using an artificial intelligence-based deep learning approach. For example, in certain embodiments, the machine learning module receives both an anatomical image (e.g., a CT image) and a functional image (e.g., a PET or SPECT image) as input, and generates, as output, a segmentation mask that identifies one or more particular target tissue regions of interest. The two images are interpreted by the machine learning module as separate channels representative of the same volume. Following segmentation, additional analysis can be performed (e.g., hotspot detection/risk assessment within the identified region of interest).

In a system and method for assessing tissue excision comprise, first 3-dimensional data is acquired for a surgical region of interest from which tissue is to be excised, the first data defining initial geometry of tissue in the region of interest. A desired excision parameter, such as depth or shape, is determined and tissue is excised from the region of interest. Second 3-dimensional data for the region of interest is then acquired, the second scan data defining post-excision geometry of the tissue in the region of interest. The first and second data is compared to determine whether the desired excision parameter has been reached. The 3-dimensional data may be scan data acquired using a 3D or 2D endoscope, and/or it may be derived from kinematic data generated as a result of moving an instrument tip over the region of interest.

Among the various aspects of the present disclosure is the provision of methods and systems for segmenting images and expediting a contouring process for MRI-guided adaptive radiotherapy (MR-IGART) comprising applying a convolutional neural network (CNN), wherein the CNN accurately segments organs (e.g., the liver, kidneys, stomach, bowel, or duodenum) in 3D MR images.

A method of visualization, characterization, and detection of objects within an image by applying a local micro-contrast convergence algorithm to a first image to produce a second image that is different from the first image, wherein all like objects converge into similar patterns or colors in the second image.

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.

A renal function assessment method includes following steps. A target kidney ultrasound image data of a subject is provided. An image pre-processing step is performed, wherein an image size of the target kidney ultrasound image data is adjusted, and the target kidney ultrasound image data is normalized according to an average and a standard deviation of a visual image database to obtain an after-processed target kidney ultrasound image data. A feature extracting step is performed, wherein the after-processed target kidney ultrasound image data is trained to achieve a convergence by a first deep-learning classifier to obtain an image feature of the after-processed target kidney ultrasound image data. A determining step is performed, wherein the image feature of the after-processed target kidney ultrasound image data is analyzed by the first deep-learning classifier to obtain an assessing result of an estimated glomerular filtration rate (eGFR).

An image processing apparatus according to the present invention includes a first classification unit configured to classify a plurality of pixels in two-dimensional image data constituting first three-dimensional image data including an object into a first class group by using a trained classifier, and a second classification unit configured to classify a plurality of pixels in second three-dimensional image data including the object into a second class group based on a result of classification by the first classification unit, the second class group including at least one class of the first class group. According to the image processing apparatus according to the present invention, a user's burden of giving pixel information can be reduced and a region can be extracted with high accuracy.