119 results about "3d segmentation" patented technology

A method for three dimensional (3D) segmentation of an object is provided. The method obtains a volumetric data set containing object data and non-object data in proximity to the object data, the object data having a reference axis extending through the object data. The method defines at least one reference slice and multiple object slices within the volumetric data set, the reference slice and the object slices intersecting one another along the reference axis and containing the reference axis. Reference points are determined within the reference slice at edges of the object data. With reference points determined, the method generates an estimated contour extended through the reference and object slices based on the reference points, the estimated contour intersecting the object slices to define estimated contour points. The method then adjusts the estimated contour points until corresponding substantially to actual contour points of the object data.

Disclosed are a system, apparatus, and method for 3D object segmentation within an environment. Image frames are obtained from one or more depth cameras or at different times and planar segments are extracted from data obtained from the image frames. Candidate segments that comprise a non-planar object surface are identified from the extracted planar segments. In one aspect, certain extracted planar segments are identified as comprising a non-planar object surface, and are referred to as candidate segments. Confidence of preexisting candidate segments are adjusted in response to determining correspondence with a candidate segment. In one aspect, one or more preexisting candidate segments are determined to comprise a surface of a preexisting non-planar object hypothesis. Confidence in the non-planar object hypothesis is updated in response to determining correspondence with a candidate segment.

The present invention refers to an angiographic image acquisition system and method which can beneficially be used in the scope of minimally invasive image-guided interventions. In particular, the present invention relates to a system and method for graphically visualizing a pre-interventionally virtual 3D representation of a patient's coronary artery tree's vessel segments in a region of interest of a patient's cardiovascular system to be three-dimensionally reconstructed. Optionally, this 3D representation can then be fused with an intraoperatively acquired fluoroscopic 2D live image of an interventional tool. According to the present invention, said method comprises the steps of subjecting the image data set of the 3D representation associated with the precalculated optimal viewing angle to a 3D segmentation algorithm (S4) in order to find the contours of a target structure or lesion to be examined and interventionally treated within a region of interest and automatically adjusting (S5) a collimator wedge position and / or aperture of a shutter mechanism used for collimating an X-ray beam emitted by an X-ray source of a C-arm-based 3D rotational angiography device or rotational gantry-based CT imaging system to which the patient is exposed during an image-guided radiographic examination procedure based on data obtained as a result of said segmentation which indicate the contour and size of said target structure or lesion. The aim is to reduce the region of interest to a field of view that covers said target structure or lesion together with a user-definable portion of the surrounding vasculature.

The invention provides a surgical puncture path intelligent automatic planning method and system based on machine learning and a medical system. The surgical puncture path intelligent automatic planning method and system based on machine learning and the medical system can rapidly determine a surgical puncture path and a needle inserting point position and can allow a brain stereotaxic apparatus or a medical mechanical arm to achieve automatic puncture surgery operation. The planning method comprises the following steps that (1) sample image data of an existing case are acquired, and trainingdata and test data are made; (2) a three-dimensional segmentation deep neural network model is designed, and the three-dimensional segmentation deep neural network model is trained; (3) the sample image data of a patient are segmented and identified by using the trained three-dimensional segmentation deep neural network model; (4) a three-dimensional model is constructed based on a segmentation identification result and the sample image data; (5) a safe needle insertion constraint area is determined based on the target point position and medical prior information; and (6), in the safe needle insertion constraint area, a three-dimensional space trajectory planning algorithm is utilized to complete surgical puncture path planning.

A method is provided for improving a segmentation of a 3D image and / or validating a segmentation of a 3D image includes rendering an acquired 3D image and a segmentation of the acquired 3D image on a segmentation display that has at least one spatially fixed slice and an interactive slice with a reference mark corresponding to the cursor location in the spatially fixed slice or slices on the display. The method further includes utilizing an interactive user input to update image data of the interactive slice and the reference mark to coincide with the cursor in the spatially fixed slice or slices. The method further includes using the cursor and the reference mark to verify that cursor locations on the boundaries of the segmentation of the acquired 3D image correspond to object boundaries in the image data of the interactive slice.

An image processing system comprising 3D image data processing means (10) of automatic mapping a 3-D Surface Model onto the surface of an object of interest in a 3-D image, for estimating a model-based 3-D segmentation surface, comprising visualizing means (60) and further comprising means of interactive adaptation (20) of the segmentation surface to the actual surface of the object of interest including means of interactive selection (40) of a 2D data plane (DP) that intersects the 3-D segmentation surface along a 2-D Model Curve (MC), said Data Plane having a user-selected orientation with respect to said surface, which is appropriate for the user to visualize a 2-D portion called Aberrant Curve (AC)of said Model Curve to be modified; means of interactive definition of a Guiding Curve (GC) in the 2-D Data Plane; means of interactive adaptation of said Aberrant Curve (AC) to said Guiding Curve (GC); and means of further automatically adapting the 3D segmentation surface within a neighborhood of the interactively adapted Aberrant Curve. The surface Model is favorably a Mesh Model.

A system and method for using learned discriminative models to segment a border of an anatomical structure in a three dimensional (3D) image is disclosed. A discriminative probability model is computed for each voxel in the 3D image. Thresholding is performed on each discriminative probability model. One or more two dimensional (2D) slices of the thresholded 3D image along X-Y planes are obtained. Seed regions are selected in the 2D slices. Morphological region growing is performed on the selected seed regions. An initial 3D segmentation is obtained. Boundary evolution is performed on the initial 3D segmentation. The segmented anatomical structure is removed. in the original 3D image.

A general purpose method to segment any kind of lesions in 3D images is provided. Based on a click or a stroke inside the lesion from the user, a distribution of intensity level properties is learned. The random walker segmentation method combines multiple 2D segmentation results to produce the final 3D segmentation of the lesion.

A system and method for using learned discriminative models to segment a border of an anatomical structure in a three dimensional (3D) image is disclosed. A discriminative probability model is computed for each voxel in the 3D image. Thresholding is performed on each discriminative probability model. One or more two dimensional (2D) slices of the thresholded 3D image along X-Y planes are obtained. Seed regions are selected in the 2D slices. Morphological region growing is performed on the selected seed regions. An initial 3D segmentation is obtained. Boundary evolution is performed on the initial 3D segmentation. The segmented anatomical structure is removed. in the original 3D image.

A method for segmenting a tubular structure includes providing a three-dimensional image containing the tubular structure, providing at least one seed point within the tubular structure, fitting an initial cylinder into the tubular structure at the seed point, adding cylinder segments to the initial cylinder in forward and backward directions within the three-dimensional image of the tubular structure by tracking the cylinder model over the tubular structure, modeling each cylinder segment using parameters to account for a three-dimensional orientation, a radius, a length and a curvature, describing an intensity image of the tubular structure using a second model to represent an edge and intensity distribution of the tubular structure, fitting the parameters as the cylinder segment is added to the intensity image, and outputting cylinder segments as a segmentation of the tube.

Presented herein are systems and methods that provide for improved 3D segmentation of nuclear medicine images using an artificial intelligence-based deep learning approach. For example, in certain embodiments, the machine learning module receives both an anatomical image (e.g., a CT image) and a functional image (e.g., a PET or SPECT image) as input, and generates, as output, a segmentation mask that identifies one or more particular target tissue regions of interest. The two images are interpreted by the machine learning module as separate channels representative of the same volume. Following segmentation, additional analysis can be performed (e.g., hotspot detection / risk assessment within the identified region of interest).

A robotic system includes a robot having an associated workspace; a vision sensor constructed to obtain a 3D image of a robot scene including a workpiece located in the workspace; and a control system communicatively coupled to the vision sensor and to the robot. The control system is configured to execute program instructions to filter the image by segmenting the image into a first image portion containing substantially only a region of interest within the robot scene, and a second image portion containing the balance of the robot scene outside the region of interest; and by storing image data associated with the first image portion. The control system is operative to control movement of the robot to perform work, on the workpiece based on the image data associated with the first image portion.