Systems, devices, methods, and computer processing products for automatically checking for errors in segmentation (contouring) using heuristic and/or statistical evaluation methods.

Methods and systems for autonomous cardiac mapping are disclosed. An example system for autonomous cardiac mapping of a heart chamber includes a processor being configured to acquire a representative geometric shell of the heart chamber, control a robotic device to autonomously navigate a mapping probe to a plurality of locations within the heart chamber based at least in part on the representative geometric shell, and generate a three-dimensional electroanatomical map of the heart chamber based on electrical data collected by the probe at the plurality of locations.

Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.

A site specifying device, includes a memory; and a processor coupled to the memory and the processor configured to: store three-dimensional model data indicating a three-dimensional model of an object, display the three-dimensional model based on the three-dimensional model data, and select from the three-dimensional model a site in a range of a depth specified toward an inner side of the three-dimensional model from a region surrounded by a closed curve on a surface of the three-dimensional model according to an input of the closed curve to the surface of the displayed three-dimensional model and an input to specify the depth from the surface of the three-dimensional model.

Various embodiments are directed to video-based deep learning evaluation of cardiac ultrasound that accurately identify cardiomyopathy and predict ejection fraction, the most common metric of cardiac function. Embodiments include systems and methods for analyzing images obtained from an echocardiogram. Certain embodiments include receiving video from a cardiac ultrasound of a patient illustrating at least one view the patient's heart, segmenting a left ventricle in the video, and estimating ejection fraction of the heart. Certain embodiments include at least one machine learning algorithm.

A model training device according to an embodiment of the present disclosure includes processing circuitry. The processing circuitry is configured to obtain an initial learning model by learning a data set including medical images as learning data. The processing circuitry is configured to evaluate the initial learning model by using a global metric, so as to obtain error data sets each having an outlier from among a plurality of data sets used in the evaluation. The processing circuitry is configured to obtain a plurality of error data set groups by grouping the plurality of error data sets while using a local metric. The processing circuitry is configured to specify model training information with respect to each of the error data set groups.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

A method is provided for measuring or estimating stress distributions on heart valve leaflets by obtaining three-dimensional images of the heart valve leaflets, segmenting the heart valve leaflets in the three-dimensional images by capturing locally varying thicknesses of the heart valve leaflets in three-dimensional image data to generate an image-derived patient-specific model of the heart valve leaflets, and applying the image-derived patient-specific model of the heart valve leaflets to a finite element analysis (FEA) algorithm to estimate stresses on the heart valve leaflets. The images of the heart valve leaflets may be obtained using real-time 3D transesophageal echocardiography (rt-3DTEE). Volumetric images of the mitral valve at mid-systole may be analyzed by user-initialized segmentation and 3D deformable modeling with continuous medial representation to obtain, a compact representation of shape. The regional leaflet stress distributions may be predicted in normal and diseased (regurgitant) mitral valves using the techniques of the invention.

An ultrasonic imaging system produces more diagnostic cardiac images of the left ventricle by plotting the longitudinal medial axis of the chamber between the apex and mitral valve plane as a curved line evenly spaced between the opposite walls of the myocardium. Transverse image planes are positioned orthogonal to the curved medial axis with control points positioned in the short axis view on lines evenly spaced around and emanating from the medial axis. If the short axis view is of an oval shaped chamber the transverse image is stretched to give the heart a more rounded appearance resulting in better positioning of editing control points.