A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain a measurement value related to the shape of a heart valve in medical image data. The processing circuitry is configured to cause a display to display a numerical value related to the shape of an artificial valve to be placed for the heart valve.

Provided is an ultrasound imaging device and an operation method thereof. An embodiment of the present disclosure provides an ultrasound imaging device comprising: an ultrasound probe; a display unit; a memory for storing at least one instruction; and a processor for executing the at least one instruction stored in the memory, wherein the processor: image-processes an echo signal to acquire multiple ultrasound images; calculates suitability indicating, as a numerical value, whether the multiple acquired ultrasound images are suitable as input images for a diagnosis algorithm for diagnosing a lesion; displays the calculated suitability on the display unit; and determines an input ultrasound image, which is to be input into the diagnosis algorithm, among the multiple ultrasound images based on the suitability.

A system for deploying needles in tissue includes a controller and a visual display. A treatment probe has both a needle and tines deployable from the needle which may be advanced into the tissue. The treatment probe also has adjustable stops which control the deployed positions of both the needle and the tines. The adjustable stops are coupled to the controller so that the virtual treatment and safety boundaries resulting from the treatment can be presented on the visual display prior to actual deployment of the system.

There are provided an acoustic wave image generating apparatus for generating a B-mode image having a fixed brightness and a control method thereof. First brightness information (81) indicating the brightness of a first B-mode image in the depth direction of the subject is generated. Positional deviation correction is performed on an acoustic wave echo signal having a positional deviation between the focusing position of acoustic waves and the observation target position, and second brightness information (82) indicating the brightness in the depth direction of the subject is generated from a superposition signal obtained by superimposing an acoustic wave echo signal for which the positional deviation has been corrected and an acoustic wave echo signal without positional deviation. The brightness of the first B-mode image is corrected based on the first brightness information and the second brightness information.

The present invention is directed to a method for health monitoring using one or more sensors comprising first measuring (100) a body composition via one or more sensors. The measured body composition is then classified (102) into one of a plurality of categories. An at least one setting to be used for the health monitoring is adjusted (104) based on the classified body composition. Then, the health monitoring is performed (106) using the adjusted at least one health monitoring setting, wherein at least one of the sensors used to measure the body composition may also be used to perform the health monitoring.

An image analysis unit has a reliability calculation unit that calculates the reliability indicating the possibility that a lesion candidate is a lesion. A mark display control unit displays a mark reporting the lesion candidate on an ultrasonic image. In a continuous detection state where the reliability continues to meet the candidate detection conditions, the mark is continued to be displayed. During the time, the form of the mark is fixed from the time point when the candidate detection conditions are first met until a fixed form period elapses. The form of the mark is then changed as the reliability varies.

Methods and systems are provided for tracking anatomical features across multiple images. One example method includes outputting, for display on a display device, an annotation indicative of a first location of an identified anatomical feature of a first ultrasound image, the annotation generated based on a first output of a model and outputting, for display on the display device, an adjusted annotation based on a second output of the model, the second output of the model generated based on a second ultrasound image and further based on the first output of the model, the adjusted annotation indicative of a second location of the identified anatomical feature in the second ultrasound image.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for contrast enhanced ultrasound quantification imaging are provided. One of the methods includes: obtaining, for each location in a region of interest, a time-dependent ultrasound signal with respect to the region of interest for a time period; determining, for the each location, a processed time-dependent ultrasound signal based at least on the obtained time-dependent ultrasound signal, wherein: the processed time-dependent ultrasound signal maps one or more portions of the obtained time-dependent ultrasound signal that each locally increase over a threshold, and the processed time-dependent ultrasound signal flattens one or more portions of the obtained time-dependent ultrasound signal that do not each locally increase over the threshold; and generating a sequence of images of the region of interest based at least on the processed time-dependent ultrasound signal of the each location, wherein the generated sequence of images displays signal variations of the processed time-dependent ultrasound signal in one or more structures in the region of interest to render the one or more structures.

A system is described for generating diagnostic information from a video sequence of ultrasound images acquired in “blind sweeps”, i.e., without operator seeing ultrasound images as they are acquired. We disclose two different types of machine learning systems for predicting diagnostic information: a “Temporal Accumulation” system and a “3-D Modeling Component” system. These machine learning systems could be implemented in several possible ways: using just one or the other of them in any given implementation, or using both of them in combination. We also disclose a computing system which implements (a) an image selection system including at least one machine learning model trained to identify clinically suitable images from the sequence of ultrasound images and (b) an image diagnosis/measurement system including of one or more machine learning models, configured to obtain the clinically suitable images identified by the image selection system and further process such images to predict health states.

An ultrasound diagnostic apparatus and an ultrasonic probe are provided which are capable of reducing a leak current even in combined use of a low voltage transmission circuit and a high voltage transmission circuit including a buffer. The ultrasonic probe includes: a high voltage transmission circuit for transmitting a relatively high voltage; a low voltage transmission circuit for transmitting a relatively low voltage; and a transducer for selectively receiving a voltage transmitted from the high voltage transmission circuit and the low voltage transmission circuit. The high voltage transmission circuit includes: a current source for varying a current generated thereby and a current drawn thereby based on a set signal; a buffer for driving the transducer in accordance with the currents generated and drawn by the current source; and an impedance controller connected to an input terminal and an output terminal of the buffer.