This invention relates to a method for training a neural network, comprising detecting a hole in each training image of a plurality of training images; transforming each training image into a transformed image, to suppress non-crack information in the training image; and training a neural network using the transformed images, to detect cracks in images (i.e. in objects in images).

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.

Systems and method for automatically generating structures, such as target volumes, in a treatment image using structure-guided deformation to propagate the structures from a planning image onto the subsequently acquired treatment image.

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.

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.

A method includes, in a processor, receiving example representations of geometrical shapes of a given type of organ. In a training phase, a neural network model is trained using the example representations. In a modeling phase, the trained neural network model is applied to a set of location measurements acquired in an organ of the given type, to produce a three-dimensional model of the organ.

An object volume acquisition method of an ultrasonic image, for a probe of an ultrasonic system is disclosed. The volume acquisition method of the object in the ultrasonic image includes collecting, by the probe, a plurality of two-dimensional ultrasonic images; obtaining the plurality of two-dimensional ultrasonic images, an offset angle, a rotation axis and a frequency of the probe corresponding to the plurality of two-dimensional ultrasonic images; segmenting a first image including an ultrasonic image object from each two-dimensional ultrasonic image of the plurality of two-dimensional ultrasonic images based on a deep learning structure; determining a contour of the ultrasonic image object; reconstructing a three-dimensional model corresponding to the ultrasonic image object according to the contour of the ultrasonic image object corresponding to the each two-dimensional ultrasonic image; and calculating a volume of the ultrasonic image object according to the three-dimensional model corresponding to the ultrasonic image object.

An object volume acquisition method of an ultrasonic image, for a probe of an ultrasonic system is disclosed. The volume acquisition method of the object in the ultrasonic image includes collecting, by the probe, a plurality of two-dimensional ultrasonic images; obtaining the plurality of two-dimensional ultrasonic images, an offset angle, a rotation axis and a frequency of the probe corresponding to the plurality of two-dimensional ultrasonic images; segmenting a first image including an ultrasonic image object from each two-dimensional ultrasonic image of the plurality of two-dimensional ultrasonic images based on a deep learning structure; determining a contour of the ultrasonic image object; reconstructing a three-dimensional model corresponding to the ultrasonic image object according to the contour of the ultrasonic image object corresponding to the each two-dimensional ultrasonic image; and calculating a volume of the ultrasonic image object according to the three-dimensional model corresponding to the ultrasonic image object.

A blood vessel detecting apparatus and an image-based blood vessel detecting method are provided. In the method, first to-be-evaluated data is detected through a first detecting model to obtain a first detection result. Second to-be-evaluated data is detected through a second detecting model to obtain a second detection result. The first to-be-evaluated data includes one or more medical images obtained from photographing a blood vessel. The first detection result output by the first detecting model includes one or more pixels in the medical image belonging to the blood vessel. The first detecting model and the second detecting model are constructed based on a machine learning algorithm. The second to-be-evaluated data includes the first detection result. The second detection result output by the second detecting model includes one or more pixels in the medical image belonging to the blood vessel.