A method for providing real-time feedback and semantic-rich guidance on ultrasound image quality is performed by a processor in an ultrasound system. The method includes receiving an ultrasound image and classifying the ultrasound image into one or more categories based on image features. The classifying creates a classified image. The method also includes determining whether the classified image provides an acceptable representation of a target organ. In response to determining that the classified image does not provide an acceptable representation of the target organ, the method includes selecting operator guidance corresponding to the one or more category; presenting via a display and/or audible sound, the selected operator guidance; and receiving additional ultrasound images. The method further includes calculating a result based on the classified image in response to determining that the classified image provides an acceptable representation of the target organ.

For segmentation in medical imaging, a shape generative adversarial network (shape GAN) is used in training. By including shape information in a lower dimensional space than the pixels or voxels of the image space, the network may be trained with a shape loss or optimization. The adversarial loss and the shape loss are used to train the network, so the resulting generator may segment complex shapes in 2D or 3D. Other optimization may be used, such as using a loss in image space.

A method comprises displaying, via an interactive interface, a medical scan and a plurality of prompts of each prompt decision tree of a plurality of prompt decision trees in succession, beginning with automatically determined starting prompts of each prompt decision tree, in accordance with corresponding nodes of each prompt decision tree until a leaf node of each prompt decision tree is ultimately selected. Labeling data indicating the ultimately selected leaf node of each prompt decision tree is determined for the medical scan.

An exemplary system, method and computer-accessible medium for detecting an anomaly(ies) in an anatomical structure(s) of a patient(s) includes receiving imaging information related to the anatomical structure(s) of the patient(s), classifying a feature(s) of the anatomical structure(s) based on the imaging information using a neural network (s), and detecting the anomaly(ies) based on data generated using the classification procedure.

Aspects of the technology described herein relate to techniques for calculating, during imaging, a quality of a sequence of images collected during the imaging. Calculating the quality of the sequence of images may include calculating a probability that a medical professional would use a given image for clinical evaluation and a confidence that an automated analysis segmentation performed on the given image is correct. Techniques described herein also include receiving a trigger to perform an automatic measurement on a sequence of images, calculating a quality of the sequence of images, determining whether the quality of the sequence of images exceeds a threshold quality, and performing the automatic measurement on the sequence of images based on determining that the quality of the sequence of images exceeds the threshold quality.

A signal processing method for performing processing of logarithmic compression with base k on to-be-processed signal (k being greater than 1), comprising: acquiring value of integer part of a result of logarithmic compression corresponding to the to-be-processed signal; calculating fractional part evaluation parameter Q according to the to-be-processed signal and the value of the integer part, Q=d*k.sup.T−N, d being value of the to-be-processed signal and greater than 0, N being the value of the integer part, T being a preset constant; evaluating, according to the fractional part evaluation parameter and a preset correspondence table, value M of a fractional part corresponding to the fractional part evaluation parameter, the correspondence table having different fractional part evaluation parameters and values of the fractional part corresponding to the fractional part evaluation parameters; obtaining the result of logarithmic compression according to value N of the integer part and value M of the fractional part.

Provided are a method of displaying a Doppler image and an ultrasound diagnosis apparatus for performing the method. The method includes: obtaining a first Doppler signal where clutter filtering corresponding to each of a plurality of pixels is not performed and a second Doppler signal where clutter filtering corresponding to each of the plurality of pixels is performed; determining a first motion score indicating a degree of flash artifact occurrence by using velocity information of the first Doppler signal; determining a first weight for suppressing flash artifacts of each pixel based on the first motion score and a velocity difference value between the first Doppler signal and the second Doppler signal; generating a first Doppler image of the object by applying the first weight to the second Doppler signal of each pixel; and displaying the first Doppler image of the object.

An ultrasound probe (104) includes a probe head (134). The probe head includes a transducer array (136) with a transducing surface (137), an instrument guide (142), and a light source (140). A method includes emitting a light beam, from a light source disposed on and adjacent to a transducer array of an ultrasound imaging probe, in a direction opposite of a transducing surface of the transducer array, at an inside wall of a cavity of a subject or object. A laparoscopic ultrasound imaging probe includes a shaft, a body, an articulating member that couples the probe head, and a handle coupled to the elongate shaft. The articulating probe head includes a transducer array that generates an ultrasound signal that traverses an image plane of the transducer array, an instrument guide, and a light source arranged to emit light in a direction opposite of the image plane.

An analysis method for breast image and an electronic apparatus using the same are provided. The method includes the following steps. A breast image scanned by an ultrasound wave is obtained. Based on rectangular features of the breast image, a region of interest including an aberrant region in the breast image is obtained by applying a detection model. The aberrant region is further acquired from the region of interest, and a plurality of feature parameters of the aberrant region are extracted for a property analysis of the aberrant region.

A system includes a probe configured to transmit ultrasound signals directed to a target blood vessel and receive echo information associated with the transmitted ultrasound signals. The system also includes at least one processing device configured to process the received echo information and generating a three-dimensional ultrasound image of the target blood vessel; obtain a three-dimensional vascular model corresponding to the target blood vessel; identify a best-fit of the three-dimensional vascular model onto the three-dimensional target image; store the best fit of the three-dimensional vascular model as a segmentation result; and calculate, based on the segmentation result, measurements for the target blood vessel.