A system is configured to perform operations includes accessing a set of model points of a model of an anatomic structure of a patient, the model points being associated with a model space. A set of measured points of the anatomic structure of the patient are collected, the measured points being associated with a patient space. The set of model points are registered to the set of measured points using a first set of initial parameters to generate a first transformation. One or more sets of perturbed initial parameters are generated based on the first set of initial parameters. One or more perturbed registration processes are performed to register the set of model points to the set of measured points using the one or more sets of perturbed initial parameters respectively to generate corresponding perturbed transformations. A registration quality indicator is generated based on the first transformation and the one or more perturbed transformations.

A method for selecting one or more targets for non-invasively treating a cardiac arrhythmia in a patient includes receiving a mapping associated with the patient's heart and generating a segmented model of the mapping associated with the patient's heart. The segmented model divides the mapping into a plurality of segments. The method includes identifying one or more abnormality in the segmented model of the mapping associated with the patient's heart, determining which segment or segments of the plurality of segments include the identified one or more abnormality, and selecting a target for non-invasive treatment of the cardiac arrhythmia based on the determined segment or segments of the plurality of segments that include the identified one or more abnormality.

The present disclosure provides a method for generating a bounding box and an apparatus for generating a bounding box, a device and a storage medium, which relate to the field of artificial intelligence, and in particular, to the technical fields of computer vision, cloud computing, intelligent search, Internet of Vehicles, and intelligent cockpits. The specific implementation solution is as follows: acquiring a depth map to be processed and depth information corresponding to the depth map; capturing a selection action by a user for a target object on the depth map; then, based on the selection action, determining, in the depth information, boundary point cloud information of the target object; and finally, based on the boundary point cloud information, generating a bounding box of the target object.

A method, a computing system and a computer program product for collecting and labelling images includes capturing a video of an object with a camera. A movement trace of a pointer is recorded that outlines the object while capturing the video of the object. Further included is generating a labeled image based at least on the captured video of the object and the recorded movement trace of the pointer. The labeled image includes the object and a line that surrounds the object.

A system for patient cardiac imaging and tissue modeling. The system includes a patient imaging device that can acquire patient cardiac imaging data. A processor is configured to receive the cardiac imaging data. A user interface and display allow a user to interact with the cardiac imaging data. The processor includes fat identification software conducting operations to interact with a trained learning network to identify fat tissue in the cardiac imaging data and to map fat tissue onto a three-dimensional model of the heart. A preferred system uses an ultrasound imaging device as the patient imaging device. Another preferred system uses an MRI or CT image device as the patient imaging device.

A quantification system (700) is described that includes: at least one input (710) configured to provide two input medical images and two locations of interest in said input medical images that correspond to a same anatomical region; and a mapping circuit (725) configured to compute a direct quantification of change of said input medical images from the at least one input (710).

An image processing apparatus comprises a changing unit configured to change a display area of an image from a first display area to a second display area including at least a portion of the first display area, an acquiring unit configured to acquire a first value indicating luminance, in which brightness contrast is considered, in an image displayed in the first display area and a second value indicating luminance, in which brightness contrast is considered, in an image displayed in the second display area, and a correcting unit configured to correct luminance of the image displayed in the second display area based on the first value and the second value that are acquired by the acquiring unit.

A computer-implemented method for high resolution image inpainting comprising the following steps: providing a high resolution input image, providing at least one inpainting mask, selecting at least one rectangular sub-region of the input image and at least one aligned rectangular subregion of the inpainting mask such that the rectangular subregion of the input image encompasses at least one set of pixels to be removed and synthetized, the at least one sub-region of the input image and its corresponding aligned subregion of the inpainting mask having identical minimum possible size and a position for which a calculated information gain does not decrease, processing the sub-region of the input image and its corresponding aligned subregion of the inpainting mask by a machine learning model, generating an output high resolution image comprising the inpainted sub-region.

A computer-implemented method comprises the following steps. In one step an image is acquired which is captured in the context of a microscopy measurement and images a sample to be examined. In one step the microscopy measurement is monitored in an automated manner. On the basis of the automated monitoring of the microscopy measurement, one or more labels are created, wherein said one or more labels comprise semantic context information of the microscopy measurement. On the basis of the image as input and said one or more labels as ground truth, a machine-learned algorithm is trained which provides semantic context information on the basis of images captured in the context of microscopy measurements. In a further step a further image is acquired, which is captured in the context of the microscopy measurement or a further microscopy measurement by the microscope and images the sample or a further sample. In a further step the trained machine-learned algorithm is applied to the further image in order to predict further semantic context information for the further image.

Disclosed are methods, systems, and apparatus for adjusting areas of interest for motion detection in camera scenes. A method includes obtaining a map of false motion event detections using a first area of interest; identifying an overlap area between the map of false detections and the first area of interest; determining a second area of interest that includes portions of the first area of interest and excludes at least a part of the overlap area; obtaining a map of true motion event detections using the first area of interest; determining whether true detections using the second area of interest compared to true detections using the first area of interest satisfies performance criteria; and in response to determining that true detections using the second area of interest compared to true detections using the first area of interest satisfies performance criteria, providing the second area of interest for use in detecting events.