Certain embodiments involve angular snapping of a target graphical object to a position in a digital artboard. For instance, a computing system determines a reference angle identifying an orientation of a target graphical object to be placed within an input graphic. The computing system also defines a tolerance region that overlaps the target graphical object and extends along an axis parallel to the reference angle. The computing system determines that at least two graphical objects are within the tolerance region. The computing system computes, for the target graphical object, a placement position on the axis. The placement position is computed based on a distance between the reference graphical objects. The computing system updates the graphical interface by placing the target graphical object within the input graphic at the placement position.

A medical registration training component executing within a medical registration system performs a training medical registration operation on a pair of medical studies. Responsive to the medical registration training system determining that the training medical registration operation succeeds, the medical registration training system records a medical registration instance for the pair of medical studies in a medical registration history and marks the medical registration instance as a positive instance in the medical registration history. Responsive to the medical registration training system determining that the training medical registration operation requires correction, the medical registration training system records a medical registration instance for the pair of medical studies in the medical registration history and marks the medical registration instance as a negative instance in the medical registration history. The medical registration training system trains a failure prediction machine learning model based on the medical registration history using machine learning such that the failure prediction machine learning model predicts whether a new medical registration operation will require correction. Responsive to the failure prediction machine learning model predicting that the new medical registration operation will require correction, the mechanism takes steps to automatically correct the new medical registration operation.

A method and apparatus of processing and displaying images captured using an in vivo capsule camera are disclosed. One or more overlapped areas between a target image and each image in a neighboring image group are determined, which comprises at least two neighboring images around the target image. Marked pixels in the target image are then determined, where a pixel in the target image is designated as a marked pixel if the pixel is within an overlapped area between the target image and at least one neighboring image. If the total number of the marked pixels in the target image exceeds a threshold and the number of the marked pixels associated with the overlapped area(s) between the target image and any image in the neighboring image group is below the threshold, the target image is excluded from a set of images to be displayed on a display device.

Disclosed is an approach to implement snapping techniques that aid the interactive, assisted, or automatic placement of layout instances or groups of layout instances for generating a legal placement layout while reducing or entirely eliminating any subsequent or separate performance of design rule checking with respect to the relevant design rules, constraints, or requirements governing the legality of the instances or groups of instances placed in the placement layout.

Described herein are systems and methods for synthetic CT image creation that allow MR-only radiotherapy of cancer patients, e.g., head and neck (H&N) cancer patients, prostate cancer patients, patients with cancer of the pelvis, abdomen cancer patients, patients with cancer of the extremities, brain cancer patients, or thorax cancer patients. The methods and systems described herein feature image processing techniques that improve the similarity between CT and MR images prior to CT-MR image registration, as well as standardization of the MR intensity histograms prior to MR-MR registration. Application of the techniques result in more accurate assignment of the Hounsfield unit to each point in the synthetic CT compared to other atlas-based methods, providing for more accurate dosing in MR-only radiotherapy simulation and planning.

Certain embodiments involve angular snapping of a target graphical object to a position in a digital artboard. For instance, a computing system determines a reference angle identifying an orientation of a target graphical object to be placed within an input graphic. The computing system also defines a tolerance region that overlaps the target graphical object and extends along an axis parallel to the reference angle. The computing system determines that at least two graphical objects are within the tolerance region. The computing system computes, for the target graphical object, a placement position on the axis. The placement position is computed based on a distance between the reference graphical objects. The computing system updates the graphical interface by placing the target graphical object within the input graphic at the placement position.

The present disclosure relates to a method for use in adaptive radiotherapy and a treatment planning device. The method may comprise accessing a first medical image and a second medical image that represent a region of interest of a patient at different times. Each medical image is segmented into a target region and at least one non-target region. The method may further comprise accessing a deformation vector field including a plurality of vectors, wherein each vector defines a geometric transformation to map a respective voxel in the first medical image to a corresponding voxel in the second medical image. The method may further comprise generating a modified deformation vector field by: identifying a first vector in the deformation vector field that maps a voxel in the first medical image to a voxel that is in a non-target region in the second medical image; and determining whether the first vector causes a distance between the mapped voxel and the target region to increase and, if so, reducing the magnitude of the first vector. The method may further comprise post-processing the modified deformation vector field to compensate for changes in the shape or size of the target region.

A surround view system for a machine is provided. The surround view system includes a plurality of image capturing devices generating image data of surroundings of the machine, and an object detection system for detecting an object in a target field of view of the machine and generating object position data corresponding to the object. The surround view system also includes an image processing system configured to generate an initial 3-dimensional composite surround view by projecting the image data on a virtual model corresponding to the machine. The virtual model has a 3-dimensional shape based on an initial calibration position data. The image processing system is also configured to modify the 3-dimensional shape of the virtual model based on the object position data, and generate an updated 3-dimensional composite surround view by projecting the image data on the virtual model having a modified 3-dimensional shape.

A data processing unit receives a reference image (IMG1.sub.3D) of a deformable physical entity, a target image (IMG2.sub.3D) of said physical entity, and a first region of interest (ROI1.sub.3D) defining a first volume in the reference image (IMG1.sub.3D) representing a reference image element. The reference image (IMG1.sub.3D), the target image (IMG2.sub.3D) and the first region of interest (ROI1.sub.3D) all contain 3D datasets. In response to user commands (c1; c2), the data processing unit defines a first contour (C1.sub.2D) in a first plane through the target image (IMG2.sub.3D), which is presented to a user via a display unit together with graphic data reflecting the reference image (IMG1.sub.3D), the target image (IMG2.sub.3D) and the first region of interest (ROI1.sub.3D). The first contour (C1.sub.2D) is aligned with at least a portion of a first border (IEB1) of a target image element (IE.sub.3D) in the target image (IMG2.sub.3D). The target image element (IE.sub.3D) corresponds to the reference image element in the reference image (IMG1.sub.3D). Based on the first contour (C1.sub.2D), the target image (IMG2.sub.3D) and the first region of interest (ROI1.sub.3D); the data processing unit determines a second region of interest (ROI2.sub.3D) defining a second volume in the target image (IMG2.sub.3D).

Various aspects of a system and a method to provide assistance in a surgery in presence of tissue deformation are disclosed herein. In accordance with an embodiment, the system includes an electronic device that receives one or more tissue material properties of a plurality of surface structures of an anatomical portion. One or more boundary conditions associated with the anatomical portion may also be received. Surface displacement of the anatomical portion may be determined by matching a first surface of the anatomical portion before deformation with a corresponding second surface of the anatomical portion after the deformation. The volume displacement field of the anatomical portion may be computed based on the determined surface displacement, the received one or more tissue material properties, and the received one or more boundary conditions.