One or more example embodiments of the present invention relates to a computer-implemented method for providing an outline of a lesion in digital breast tomosynthesis includes receiving input data, wherein the input data comprises a reconstructed tomosynthesis volume dataset based on projection recordings, a virtual target marker within a lesion being in the tomosynthesis volume dataset; applying a trained function to at least a part of the tomosynthesis volume dataset to establish an outline enclosing the lesion, the part of the tomosynthesis volume dataset corresponding to a region surrounding the virtual target marker in the tomosynthesis volume dataset; and providing output data, wherein the output data is an outline of a two-dimensional area or a three-dimensional volume surrounding the target marker.

The album creation application of the present disclosure displays image data, to which trimming is performed, and a template, which includes a slot in which the image data is arranged, so that a slot and image data to be arranged in the slot are selected by use of an input device. Position information of a point of interest in the image to be arranged in the slot is obtained. Composition patterns applicable to the image with designation of the point of interest are presented to the user, and the composition pattern to be applied, which is selected by the user from among the presented composition patterns, is obtained. Trimming is performed based on the point of interest and the selected composition pattern selected. The trimmed images are listed, so that multiple trimmed images are presented to the user as trimming proposals.

The invention relates to a system and computer-implemented method for enabling correction of a segmentation of an anatomical structure in 3D image data. The segmentation may be provided by a mesh which is applied to the 3D image data to segment the anatomical structure. The correction may for example involve a user directly or indirectly selecting a mesh part, such as a mesh point, that needs to be corrected. The behaviour of the correction, e.g., in terms of direction, radius/neighbourhood or strength, may then be dependent on the mesh normal direction, and in some embodiments, on a difference between the mesh normal direction and the orientation of the viewing plane.

The invention relates to a medical data processing method for determining the position of a region of interest serving as a start condition for conducting diffusion image-based tracking of nerve fibers. In one example, the method encompasses comparing a set of tracked nerve fibers to a model of nerve fibers contained in atlas data.

The invention relates to a medical data processing method for determining the position of a nerve fiber based on a diffusion image-based tracking method of tracking nerve fibers. In one example, the method encompasses comparing a set of tracked nerve fibers to a model of nerve fibers contained in atlas data.

A computer system maintains structure data indicating geometrical constraints for each structure category of a plurality of structure categories. The computer system generates a virtual 3D representation of a structure based on a set of images depicting the structure. For each image in the set of images, one or more landmarks are identified. Based on the landmarks, a candidate structure category is selected. The virtual 3D representation is generated based on the geometrical constraints of the candidate structure category and the landmarks identified in the set of images.

In order to reduce a radiation dose delivered to an object or an observer, a facility for monitoring handling of the object has an optical unit configured to direct ionizing radiation onto the object and also a filter element in order to attenuate a part of the ionizing radiation. An imaging unit may detect portions of the ionizing radiation passing through the object in order to create an image of the object. A view acquisition system may acquire a viewing movement, and a control unit is configured, during a first operating mode, to control a position of the filter element as a function of the viewing movement. The control unit is configured to identify a predefined sequence of viewing movements and, as a function thereof, to switch into a second operating mode. The position of the filter element is controlled during the second operating mode as a function of an image analysis.

In one embodiment, a method includes accessing a plurality of image frames captured by one or more cameras, classifying one or more first objects detected in one or more first image frames of the plurality of image frames as undesirable, applying a pixel filtering to the one or more first image frames to replace one or more first pixel sets associated with the one or more first objects with pixels from one or more second image frames of the plurality of image frames to generate a final image frame, providing the final image frame for display.

Systems and methods are provided for improved intra-operative micro-CT imaging of explanted tissue samples and for improved visualization of such samples. These embodiments provide for reduced scan times and the ability for radiologists to quickly receive useful scan imagery and to provide accurately-communicated recommendations to the operating surgeon. Improved scan visualization methods facilitate surgeon and radiologist interaction with the scan data, including of annotation, viewing, and reorientation to accurately reflect the orientation of imaged tissue samples relative to the body prior to explantation. Improved visualization methods include color-coded sample texturing to indicate sample orientation, color-coded tumor visualization to indicate proximity to sample margins, and intuitive methods for adjusting the location and orientation of two-dimensional visualizations relative to the sample.

Disclosed are an apparatus and method for medical image processing according to pathologic lesion properties, the method including: recognizing a readout area different from an original readout area in a medical image by applying a previously trained deep learning model to the medical image, extracting properties, which include at least one of a location and a size of the readout area, from the medical image, and generating a readout image for the readout area, which is different from the original readout area corresponding to a purpose of taking the medical image, by reconstructing the medical image, thereby having an effect on generating a readout image for a different kind of pathologic lesion from a previously acquired medical image.