An ultrasound imaging device and a clutter filtering method using the same are disclosed. The clutter filtering method using the ultrasound imaging device according to one embodiment includes obtaining ultrasound data from a field-of-view (FOV) of an object, generating decomposition data including common scale information by performing rank matrix decomposition once on all of the obtained ultrasound data, estimating local characteristic information by reflecting spatial information on each pixel to the common scale information, and extracting a blood flow signal by performing filtering on each pixel based on the estimated local characteristic information.

A workflow is disclosed to accurately register ultrasound imaging to co-modality imaging. The ultrasound imaging is segmented with a convolutional neural network to detect a surface of the object. The ultrasound imaging is calibrated to reflect a variation in propagation speed of the ultrasound waves through the object by minimizing a cost function that sums the differences between the first and second steered frames, and compares the first and second steered frames of the ultrasound imaging with a third frame of the ultrasound imaging that is angled between the first and second steered frames. The ultrasound imaging is temporarily calibrated with respect to a tracking coordinate system by creating a point cloud of the surface and calculating a set of projection values of the point cloud to a vector. The ultrasound imaging, segmented and calibrated, is automatically registered to the co-modality imagine.

A fully automated ultrasound apparatus includes a sensor or probe which can be initially manually attached to a side of the neck of a patient, an ultrasound interface to control the sensor and periodically acquire raw ultrasound data, a signal and image processing system to autonomously convert the raw ultrasound data into a measurement that is useful to physicians, and a display to relay the current measurements and measurement history to provide data trends. The sensor can include one or more ultrasound transducers built into a housing. A disposable component can serve to secure the sensor to the neck of the patient and to provide a coupling medium between the sensor and the skin of the patient.

According to one embodiment, an analysis apparatus includes processing circuitry. The processing circuitry configured to generate a harmonic signal and a fundamental wave signal based on a reception signal that is collected by an ultrasound probe, the harmonic signal corresponding to a harmonic component of a reflected wave of a ultrasound generated in the subject, the fundamental wave signal corresponding to a fundamental wave component of the reflected wave, calculate a first index value indicating tissue properties of the subject based on the harmonic signal, and calculate a second index value indicating the tissue properties based on the fundamental wave signal, and display an analysis result based on the first index value and the second index value.

The invention provides a method for performing an assessment of a placenta. The method includes obtaining a 3D ultrasound image of a uterus (210) and segmenting the placenta (220). A 3D rendering (200) of the uterus is then generated, wherein the generating includes: identifying a position of the placenta within the uterus with respect to an anatomical structure such as the cervix (250); obtaining anatomical reference data relating to a potential risk associated with the position of the placenta within the uterus; and comparing the position of the placenta and the anatomical reference data. A 3D rendering of the uterus is generated that comprises a 3D rendering of the placenta, marked with an indicator that is altered based on the comparison of the position of the placenta and the anatomical reference data. The appearance of the indicator may vary according to e.g. risk type/severity.

A non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process, an information processing apparatus, and a model generation method that outputs complication information for a medical treatment. The process includes acquiring a medical image obtained by imaging a lumen organ of a patient before treatment, inputting the acquired medical image into a trained model so as to output complication information on a complication that is likely to occur after the treatment when the medical image is received, and outputting the complication information. Preferably, complication information including a type of the complication that is likely to occur and a probability value indicating an occurrence probability of the complication of the type is output.

A computer is caused to perform processing of: acquiring a plurality of medical images generated based on signals detected by a catheter inserted into a lumen organ while the catheter is moving a sensor along a longitudinal direction of the lumen organ, the lumen organ including a main trunk, a side branch branched from the main trunk, and a bifurcated portion of the main trunk and the side branch; and recognizing a main trunk cross-section, a side branch cross-section, and a bifurcated portion cross-section by inputting the acquired medical images into a learning model configured to recognize the main trunk cross-section, the side branch cross-section, and the bifurcated portion cross-section.

A non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process of acquiring a medical image generated based on a signal detected by a catheter inserted to a lumen organ, estimating a position of an object at least included in the acquired medical image by inputting the medical image to a first learning model for estimating a position of an object included in the medical image, extracting from the medical image an image portion by using the estimated position of the object as a reference, and recognizing the object included in the extracted image portion by inputting the image portion to a second learning model for recognizing an object included in the image portion.

A system for screening and diagnostics of cellular tissue includes a system controller, an ultrasound apparatus, an electromechanical positioning apparatus, and an image analyzer communicably coupled together. The electromechanical positioning apparatus includes an articulated arm, and the ultrasound apparatus includes a scan head coupled to an end of the articulated arm. The system controller controls the electromechanical positioning apparatus to move the scan head adjacent the cellular tissue while controlling the ultrasound apparatus to generate a first set of ultrasound images of the cellular tissue. The image analyzer analyzes the ultrasound images, and in response to identifying a potential abnormality, the electromechanical positioning apparatus is controlled to move the scan head to a position adjacent the location of the potential abnormality, where the electromechanical positioning apparatus and the ultrasound apparatus are controlled to generate a second set of ultrasound images of the potential abnormality.

Methods for acquiring ultrasonic data from a scanner constructed for B-mode scans are disclosed. An image-acquiring system is provided. A three-dimensional target region is selected. A model of the target region comprising a plurality of target locations representing a plurality of planned locations in the target region at which ultrasonic data is to be acquired is created, and a visual representation of the model comprising a plurality of graphical elements is displayed. Ultrasonic data at each of the planned locations is acquired. A transformation of the visual representation is executed, comprising: performing a data quality test at each target location; for any target location that fails the data quality test, altering a graphical element corresponding to the failed target location to indicate failure of the data quality test at that location; and displaying a transformed visual representation comprising updated graphical elements on the visual display