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.

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.

A method for analyzing computed tomography angiography (CTA) data is provided. The method includes receiving, at a processor, three-dimensional (3D) CTA data. The method also includes automatically, via the processor, detecting objects of interest within the 3D CTA data. The method further includes generating, via the processor, a CTA image volume that only includes the objects of interest.

Image analysis methods based on an ultrasound imaging device and ultrasound imaging devices are provided. An image analysis method may include: obtaining an image of a first section of a target object; generating a first analysis result corresponding to the target object according to the image of the first section; obtaining an image of a second section of the target object; generating a second analysis result corresponding to the target object according to the image of the second section; generating a diagnostic analysis result of the target object according to the first analysis result and the second analysis result; and displaying the diagnostic analysis result of the target object.

Systems and methods are provided for registering images. The systems and methods perform operations comprising: receiving, at a first time point during a given radiation session, a first imaging slice comprising an object, the first imaging slice corresponding to a first plane; accessing, at the first time point during the given radiation session, a composite imaging slice corresponding to the first plane, the composite imaging slice being generated using a plurality of imaging slices obtained prior to the first time point; spatially registering the first imaging slice and the composite imaging slice; determining movement of the object using the spatially registered first imaging slice and the composite imaging slice; and generating an updated therapy protocol to control delivery of a therapy beam based on the determined movement.

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.

A method for analyzing computed tomography angiography (CTA) data is provided. The method includes receiving, at a processor, three-dimensional (3D) CTA data. The method also includes automatically, via the processor, detecting objects of interest within the 3D CTA data. The method further includes generating, via the processor, a CTA image volume that only includes the objects of interest.

A tangible, non-transitory, machine-readable medium including instructions presents a graphical user interface (GUI) and the GUI provides an indication of two or more output file types. Further, the medium including instructions receives an indication of a selection of at least two of the two or more output file types. Moreover, the medium including instructions generates corresponding output files for the selection of the at least two of the two or more output file types by accessing an applicable computer-aided-design (CAD) model associated with a part number. Also, the medium including instructions generates corresponding output files for the selection of the at least two of the two or more output file types.

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 system and method for planning surgical procedure including a treatment zone setting view presenting at least one slice of a 3D reconstruction generated from CT image data including a target. The treatment zone setting view presenting a treatment zone marker defining a location and a size of a treatment zone and configured to adjust the treatment zone marker in response to a received user input. The system and method further including a volumetric view presenting a 3D volume derived from the 3D reconstruction and a 3D representation of the treatment zone marker relative to structures depicted in the 3D volume.