Disclosed herein are systems and methods for providing interactive graphical user interfaces (GUIs) for users, such as medical professionals, to glean insight about connectivity data associated with a particular brain. A method can include overlaying nodes representing locations of parcels of a patient's brain on a representation of a brain and displaying the representation of the brain with the overlaid nodes in a GUI. Nodes having connectivity above a first threshold can be represented in a first indicia and nodes having connectivity below a second threshold can be represented in a second indicia. The method can include receiving user input and taking an action based on the user input. The user input can include selecting an area of the representation of the brain for excision. Taking an action based on the input can include calculating an impact of excising the area of the brain on the particular patient.

A System for image processing (IPS), in particular for lung imaging. The system (IPS) comprises an interface (IN) for receiving at least a part of a 3D image volume (VL) acquired by PAT an imaging apparatus (IA1) of a lung (LG) of a subject (PAT) by exposing the subject (PAT) to a first interrogating signal. A layer definer (LD) of the system (IPS) is configured to define, in the 3D image volume, a layer object (LO) that includes a representation of a surface (S) of the lung (LG). A renderer (REN) of the system (IPS) is configured to render at least a part of the layer object (LO) in 3D at a rendering view (V.sub.p) for visualization on a display device (DD).

The invention relates to a method of inserting an insertion object into a sequence of images. The insertion object may be an image, a video, or a three-dimensional model, which could possibly be animated. Particularly, but not exclusively, the invention relates to the insertion of advertisement images into video, such as videos of sporting events. A method comprises capturing a sequence of images, the sequence of images comprising in order a first image, a second image, and a third image; estimating a first homographic transform from the first image to the third image; deriving a second homographic transform from the first image to the second image based on the first homographic transform; transforming the insertion object using the first homographic transformation to form a first warped insertion image, and inserting the first warped insertion image into the third image of the sequence of images; and transforming the insertion object using the second homographic transformation to form a second warped insertion image, and inserting the second warped insertion image into the second image of the sequence of images.

The invention relates to a method of inserting an insertion object into a sequence of images. The insertion object may be an image, a video, or a three-dimensional model, which could possibly be animated. Particularly, but not exclusively, the invention relates to the insertion of advertisement images into video, such as videos of sporting events. A method comprises capturing a sequence of images, the sequence of images comprising in order a first image, a second image, and a third image; estimating a first homographic transform from the first image to the third image; deriving a second homographic transform from the first image to the second image based on the first homographic transform; transforming the insertion object using the first homographic transformation to form a first warped insertion image, and inserting the first warped insertion image into the third image of the sequence of images; and transforming the insertion object using the second homographic transformation to form a second warped insertion image, and inserting the second warped insertion image into the second image of the sequence of images.

A method for training a machine learning model to generate a predicted measured image, the method including obtaining (a) an input target image associated with a reference design pattern, and (b) a reference measured image associated with a specified design pattern printed on a substrate, wherein the input target image and the reference measured image are non-aligned images; and training, by a hardware computer system and using the input target image, the machine learning model to generate a predicted measured image.

A method for training a machine learning model to generate a predicted measured image, the method including obtaining (a) an input target image associated with a reference design pattern, and (b) a reference measured image associated with a specified design pattern printed on a substrate, wherein the input target image and the reference measured image are non-aligned images; and training, by a hardware computer system and using the input target image, the machine learning model to generate a predicted measured image.

A control unit of a processing apparatus detects a linear region corresponding to a first planned dividing line from an intersection region of the first planned dividing line and a second planned dividing line, obtains an angle between the linear region and an X-axis direction, and positions the linear region corresponding to the first planned dividing line in the X-axis direction. A linear region corresponding to a next first planned dividing line is detected and an interval between the first planned dividing lines is set. A second planned dividing line interval setting section detects two linear regions corresponding to second planned dividing lines, the linear regions being adjacent to each other, and an interval is set between the second planned dividing lines. A device image enclosed by a pair of first planned dividing lines and a pair of second planned dividing lines is generated and stored.

A control unit of a processing apparatus detects a linear region corresponding to a first planned dividing line from an intersection region of the first planned dividing line and a second planned dividing line, obtains an angle between the linear region and an X-axis direction, and positions the linear region corresponding to the first planned dividing line in the X-axis direction. A linear region corresponding to a next first planned dividing line is detected and an interval between the first planned dividing lines is set. A second planned dividing line interval setting section detects two linear regions corresponding to second planned dividing lines, the linear regions being adjacent to each other, and an interval is set between the second planned dividing lines. A device image enclosed by a pair of first planned dividing lines and a pair of second planned dividing lines is generated and stored.

Embodiments of the present invention provide systems and methods to detect a moving anatomic feature during a treatment sequence based on a computed and/or a measured shortest distance between the anatomic feature and at least a portion of an imaging system.

Embodiments of the present invention provide systems and methods to detect a moving anatomic feature during a treatment sequence based on a computed and/or a measured shortest distance between the anatomic feature and at least a portion of an imaging system.