Registration of a surgical image acquisition device (e.g. an endoscope) using preoperative and live contour signatures of an anatomical object is described. A control unit includes a processor configured to compare the real-time contour signature to the database of preoperative contour signatures of the anatomical object to generate a group of potential contour signature matches for selection of a final contour match. Registration of an image acquisition device to the surgical site is realized based upon an orientation corresponding to the selected final contour signature match.

An image processing apparatus and method are provided. The image processing apparatus acquires a target image including a depth image of a scene, determines three-dimensional (3D) point cloud data corresponding to the depth image based on the depth image, and extracts an object included in the scene to acquire an object extraction result based on the 3D point cloud data.

Provided is a slicing 2D data-based pattern application method for generating an output code in which the inside of a model is filled with a pattern, so as to reduce a binder usage amount and maintain a strength and a shape in sand binder jetting additive manufacturing. A slicing 2D data-based pattern application method according to an embodiment of the present invention comprises the steps of: generating 2D data by slicing an output model; generating an inner pattern in at least one of layers forming an output model in consideration of the set thickness of the layers and the outer thickness thereof and generating an output code by applying the generated inner pattern. Therefore, the cost of producing an additive manufacturing output can be reduced by reducing the binder usage amount through the application of the inner pattern. In addition, reducing the binder usage amount enables a mold to be destroyed with less force than conventionally used, can increase the proportion of molding sand reuse by reducing the binder usage amount, and can reduce recovery treatment costs of the molding sand. Furthermore, casting defects due to increased ventilation can be reduced.

Computer-implemented methods and systems for identifying corresponding slices in medical image data sets are provided. For example, the systems and methods are based on identifying corresponding slices by systematically quantifying image similarities between the slices comprised in one medical image data set and the slices comprised in another medical image data set.

A user input obtainer receives an image movement instruction including identification information specifying a position shift or an image capture time shift to be performed and also including a displacement amount. When the identification information specifies the position shift, a slice position selector determines a tomographic image at a destination of the position shift based on the displacement amount from a set of tomographic images captured at the same time. On the other hand, when the identification information specifies the image capture time shift, the image capture time selector determines a tomographic image at a destination of the shift based on the displacement amount from sets of tomographic images that are identical to each other in terms of a patient, an examination portion, and a modality. A displaying image obtainer reads out the determined tomographic image from an image storage device and gives it to a display information generator.

The present invention relates to a device (2) and a method (100) for determining at least one final two-dimensional image or slice for visualizing an object of interest in a three-dimensional ultrasound volume. The method (100) for determining at least one final two-dimensional image, the method comprises the steps: a) providing (101) a three-dimensional image of a body region of a patient body, wherein an applicator configured for fixating at least one radiation source is inserted into the body region; b) providing (102) an initial direction, in particular by randomly determining the initial direction within the three-dimensional image; c) repeating (103) the following sequence of steps s1) to s4): s1) determining (104), via a processing unit, a set-direction within the three-dimensional image based on the initial direction for the first sequence or based on a probability map determined during a previous sequence; s2) extracting (105), via the processing unit, an image-set of two-dimensional images from the three-dimensional image, such that the two-dimensional images of the image-set are arranged coaxially and subsequently in the set-direction; s3) applying (106), via the processing unit, an applicator pre-trained classification method to each of the two-dimensional images of the image-set resulting in a probability score for each of the two-dimensional images of the image-set indicating a probability of the applicator being depicted, in particular fully depicted, in the respective two-dimensional image of the image-set in a cross-sectional view; and s4) determining (107), via the processing unit, a probability-map representing the probability scores of the two-dimensional images of the image-set with respect to the set-direction; wherein the method comprises the further step: d) determining (108), via a processing unit and after finishing the last sequence, the two-dimensional image associated with the highest probability score, in particular from the image-set determined during the last sequence, as the final two-dimensional image. The invention provides an efficient way to ensure that the ultrasound volume has the required clinical information by providing the necessary scan planes having the object of interest e.g. the applicator (6) in a three-dimensional ultrasound volume.

A computer-implemented method comprising: receiving a 3D image including an object depicted in the image, the 3D image comprising an ordered set of 2D images; determining a contour around the object in a first of said 2D images; and determining a contour around the object in a second of said 2D images, the second 2D image being non-contiguous with the first in said ordered set, having an intermediate region comprising one or more intermediate ones of said 2D images between the first and second 2D images within said ordered set. In each of the first and second 2D images, inside of the contour is classified as foreground and outside of the contour is classified as background. The method further comprises performing a 3D geodesic distance computation to classify points in the intermediate region as foreground of background.

One or more devices, systems, methods, and storage mediums using artificial intelligence application(s) using an apparatus or system that uses and/or controls one or more imaging modalities, such as, but not limited to, angiography, Optical Coherence Tomography (OCT), Multi-modality OCT, near-infrared fluorescence (NIRAF), OCT-NIRAF, etc. are provided herein. Examples of AI applications discussed herein, include, but are not limited to, using one or more of: AI coregistration, AI marker detection, deep or machine learning, computer vision or image recognition task(s), keypoint detection, feature extraction, model training, input data preparation techniques, input mapping to the model, post-processing, and/or interpretation of output data, one or more types of machine learning models (including, but not limited to, segmentation, regression, combining or repeating regression and/or segmentation), marker detection success rates, and/or coregistration success rates to improve or optimize marker detection and/or coregistration.

A computer-implemented method for determining an orientation of at least one diagnostically relevant sectional plane for heart imaging in a three-dimensional magnetic resonance imaging image dataset, comprises: providing the three-dimensional image dataset; applying a trained function to the three-dimensional image dataset to determine a position of at least one landmark; determining the orientation of the at least one diagnostically relevant sectional plane as a function of at least one landmark; and providing the orientation of the at least one diagnostically relevant sectional plane.

Systems and methods are provided for registering images. The systems and methods perform operations comprising: receiving, at a first time point in a given radiation session, a first imaging slice corresponding to a first plane; encoding the first imaging slice to a lower dimensional representation; applying a trained machine learning model to the encoded first imaging slice to estimate an encoded version of a second imaging slice corresponding to a second plane at the first time point to provide a pair of imaging slices for the first time point; simultaneously spatially registering the pair of imaging slices to a volumetric image, received prior to the given radiation session, comprising a time-varying object to calculate displacement of the object; and generating an updated therapy protocol to control delivery of a therapy beam based on the calculated displacement of the object.