The present disclosure provides a non-contact heart rhythm category monitoring system, which includes steps as follows. Facial images are continuously captured through an image sensor; images of a continuous target area for a predetermined duration are extracted from the facial images; non-contact physiological signal related to heartbeats are captured from the images of the continuous target area; the non-contact physiological signal are classified into a normal heart rhythm, an atrial fibrillation and a non-atrial fibrillation arrhythmia.

In one embodiment, a medical system includes a catheter configured to be inserted into a heart of a living subject, and including electrodes configured to capture electrical activity of the heart at respective position in the heart, a display, and processing circuitry configured to receive position signals from the catheter, and in response to the position signals compute the respective positions of the electrodes, generate an anatomical map responsively to respective ones of the computed positions, find an anatomical feature of the heart and a position of the anatomical feature responsively to the respective positions of, and electrical activity captured by, respective ones of the electrodes, automatically segment the anatomical map responsively to the found position of the anatomical feature, and render the anatomical map to the display.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Embodiments of the disclosure provide methods and systems for representation learning from a biomedical image with a sparse convolution. The exemplary system may include a communication interface configured to receive the biomedical image acquired by an image acquisition device. The system may further include at least one processor, configured to extract a structure of interest from the biomedical image. The at least one processor is also configured to generate sparse data representing the structure of interest and input features corresponding to the sparse data. The at least one processor is further configured to apply a sparse-convolution-based model to the biomedical image, the sparse data, and the input features to generate a biomedical processing result for the biomedical image. The sparse-convolution-based model performs one or more neural network operations including the sparse convolution on the sparse data and the input features.

A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels, the method comprising the following steps: receiving (101) an x-ray angiography scan representing a maximum intensity projection of a region of anatomy that includes the coronary vessels on the imaging plane; preprocessing (102) the scan to output a preprocessed scan; and performing autonomous coronary vessel segmentation (103) by means of a trained convolutional neural network (CNN) that is trained to process the preprocessed scan data to output a mask denoting the coronary vessels.

The disclosure provides for a method for generating an ultrasound image that includes transmitting, by a plurality of transmitters in a transducer, at least two transmit beams at different angles, where at least parts of the transmit beams cover an overlapping region, and receiving, by a plurality of sensors of the transducer, reflected signals of the transmit beams. The method further comprises calculating channel coherence for the received signals to produce one or more channel coherence images, and calculating transmit coherence for the received signals to produce one or more transmit coherence images. The information from at least one of the channel coherence images and at least one of the transmit coherence images are combined to identify moving objects. The received signals from different transmits in overlapping regions are then processed to produce a final image that is compensated for the moving objects.

A method may include obtaining an image representing a region of interest (ROI) of an object. The ROI may include two or more sub-regions. The method may include determining an average value of quantitative indexes associated with elements in the image corresponding to a first region of the ROI. The method may include determining, for each of the two or more sub-regions of the ROI, a threshold based on the average value; identifying target elements in the image based on the thresholds of the two or more sub-regions. The method may include assigning a presentation value to each of at least some of the target elements based on the average value and the quantitative index of the each target element. The method may include generating a presentation of the image based on the presentation values.

An interpolation unit generates a vector array representing a movement destination of a representative point array. A smoothing unit smoothes a tangential component and a normal component of each vector of the vector array, to generate a smoothed vector array. An aligning unit generates a new representative point array based on the smoothed vector array. In this process, alignment is performed for each representative point sequence. A tracking image is created based on the new representative point array.

A method of quantifying a size of a tissue of interest in a animal through a device including a storage, an image processor, and a display by using an X-ray image of the animal is proposed. The proposed method may include storing the X-ray image in the storage, displaying the X-ray image on the display, performing, by the image processor, processes of (i) calculating a reference value for a length of a reference tissue of the animal displayed on the X-ray image, (ii) calculating a value of a length in at least one specific direction of the tissue of interest of the animal displayed on the X-ray image, and (iii) quantifying the size of the tissue of interest as a ratio of the value of the length to the reference value, and displaying the quantified size of the tissue of interest on the display.

In accordance with embodiments of this disclosure, a computational simulation platform for assessing impact of coronary artery bypass grafting comprises a computer-implemented method that includes: generating patient-specific three-dimensional (3D) reconstructions of path lines for a patient's heart, ascending aorta, aortic arch, descending thoracic aorta, great vessels, coronary arteries and their major branches based on noninvasive imaging; performing virtual CABG by modifying the patient-specific 3D reconstructions to computationally add path lines for one or more bypass grafts; performing post-virtual CABG computational fluid dynamic (CFD) studies under computational resting and stress conditions; and assessing hemodynamic impact of virtual CABG on the resting and hyperemic flow of diseased native coronary arteries and virtual bypass grafts.