Embodiments include a patch-type, ultrasound sensor system and method to monitor the function and motion of a patients anatomical structure, comprising processing at least one received ultrasound image using one or more analytical tools, including radon transformation, higher-order spectra techniques, and/or active contour models, to generate at least one processed ultrasound image; inputting the at least one processed ultrasound image into a deep learning Convolutional Neural Network to obtain an automatic classification result selected from two or more classes indicating the functional state of the anatomical structure. The patch-type, ultrasound sensor system can communicate via a wireless or wired connection. The monitoring can be at rest or during surgery or other procedure or whilst the subject is exposed to any physiological stressors as part of medical examinations, and can be adapted for use in monitoring the function of body structures including the heart, blood vessels, lungs or joints.

Intravascular imaging systems and methods for making and using intravascular imaging devices are disclosed. An example intravascular imaging device may comprise a catheter including an imaging device. A processor may be coupled to the catheter. The processor may be configured to process image data received from the imaging device. The processor may be configured to generate a calcium map. The calcium map may include an indicator of calcium depth to a vessel lumen surface, calcium distance to a center of the catheter, or both. A display unit may be coupled to the processor. The display unit may be configured to show a display including the calcium map.

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.

An image processing apparatus includes at least one memory storing a program, and at least one processor which, by executing the program, causes the image processing apparatus to acquire an image of a target object, and perform correction to rotate the image so that an orientation of the target object in the image aligns with a representative direction, which is determined for type of the target object.

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.

Machine learning trains to tune settings. For training, the user interactions with the image parameters (i.e., settings) as part of ongoing examination of patients are used to establish the ground truth positive and negative examples of settings instead of relying on an expert review of collected samples. Patient information, location information, and/or user information may also be included in the training data so that the network is trained to provide settings for different situations based on the included information. During application, the patient is imaged. The initial or subsequent image is input with other information (e.g., patient, user, and/or location information) to the machine-trained network to output settings to be used for improved imaging in the situation.

Embodiments of the present invention provide a method, system and computer program product for ultrasound image acquisition optimization according to different respiration modes. A method for ultrasound image acquisition optimization according to different respiration modes includes acquiring by an ultrasound imaging device, an ultrasound image of a target organ. The method further includes comparing attributes of the acquired ultrasound image to association data in a data store of associations associating attributes of previously acquired ultrasound imagery of different images of the target organ with different modes of respiration. Finally, the method includes determining from the comparison, a mode of respiration evident from the acquired ultrasound image and presenting the determined mode in the ultrasound imaging device.

Disclosed in the application is a handheld three-dimensional ultrasound imaging method, comprising using a handheld ultrasound probe to scan a part to be tested; obtaining a three-dimensional position and angle information corresponding to ultrasound images according to a positioning reference device adapted to be arranged on the part to be tested; obtaining a moving distance and a rotation angle of the handheld ultrasound probe according to a material interference of the localization pattern with the ultrasonic signal; extracting information of the localization pattern from the ultrasound image as positioning information; restoring the ultrasound image to a state without interference from the localization pattern; performing 3D image reconstruction and display of the ultrasound image. The large spatial positioning system in an existing three-dimensional ultrasound imaging system is changed into a portable spatial positioning system that can be used at any time, so that handheld three-dimensional ultrasound imaging can be widely applied.

Various methods and systems are provided for identifying connected regions in ultrasound images. An exemplary method includes acquiring ultrasound video data of an anatomical region while a force is being applied to induce movement within the anatomical region, the ultrasound video data including a plurality of ultrasound images. The method includes identifying two or more connected regions in one of the plurality of ultrasound images, generating a modified ultrasound image by graphically distinguishing the first connected region from the second connected region, and causing a display device to display the modified image.

Methods and apparatuses for providing indications of missing landmarks in ultrasound images are described. Some embodiments are directed to apparatuses comprising a processing device configured to obtain data representing an ultrasound image, and determine whether the ultrasound image is clinically usable, wherein the determining comprises determining whether the ultrasound image lacks one or more landmarks. Determining whether the ultrasound image is clinically usable may further comprise determining a quality value representative of a quality of the ultrasound image and comparing the quality value to a threshold quality value. In some embodiments, landmarks comprise one or more anatomical features, such as a rib, a pleural line and an A line, a liver, and a kidney.