Apparatus, systems, and methods to process an interior region of interest in an anatomy image via a user interface are disclosed. An example apparatus is to at least: process an image to reduce noise in the image; identify at least one of an organ of interest or a region of interest in the image; analyze values in at least one of the organ of interest or the region of interest; process the at least one of the organ of interest or the region of interest based on the analyzed values to provide a processed object in the at least one of the organ of interest or the region of interest; and display the processed object for interaction via an interface, the display to include exposing at least one of the organ of interest or the region of interest.

An information processing apparatus includes a first acquisition unit configured to acquire first region information about an object on first two-dimensional tomographic image data orthogonal to a first axis of three-dimensional tomographic image data, a second acquisition unit configured to acquire second region information about the object on second two-dimensional tomographic image data orthogonal to a second axis different from the first axis of the three-dimensional tomographic image data, and a generation unit configured to generate a three-dimensional likelihood map representing objectness of the object as three-dimensional ground truth image data corresponding to the three-dimensional tomographic image data based on the first region information and the second region information.

The present disclosure relates to a medical image analysis method, a medical image analysis device, and a computer-readable storage medium. The medical image analysis method includes receiving a medical image acquired by a medical imaging device; determining a navigation trajectory by performing navigation processing on the medical image based on an analysis requirement, the analysis requirement indicating a disease to be analyzed; extracting an image block set along the navigation trajectory; extracting image features using a first learning network based on the image block set; and determining an analysis result using a second learning network based on the image features and the navigation trajectory.

The present disclosure relates to an image processing method. The method may include: obtaining image data; reconstructing an image based on the image data, the image including one or more first edges; obtaining a model, the model including one or more second edges corresponding to the one or more first edges; matching the model and the image; and adjusting the one or more second edges of the model based on the one or more first edges.

An image processing apparatus includes: an obtaining unit configured to obtain a first image of an object and a second image of the object; a difference unit configured to obtain a difference image after the first image and the second image are registered; and a change unit configured to perform processing of changing a pixel value in the difference image based on a likelihood calculated using a pixel value in the first image and a pixel value in the second image.

A method and apparatus for hierarchical parsing and semantic navigation of a full or partial body computed tomography CT scan is disclosed. In particular, organs are segmented and anatomic landmarks are detected in a full or partial body CT volume. One or more predetermined slices of the CT volume are detected. A plurality of anatomic landmarks and organ centers are then detected in the CT volume using a discriminative anatomical network, each detected in a portion of the CT volume constrained by at least one of the detected slices. A plurality of organs, such as heart, liver, kidneys, spleen, bladder, and prostate, are detected in a sense of a bounding box and segmented in the CT volume, detection of each organ bounding box constrained by the detected organ centers and anatomic landmarks. Organ segmentation is via a database-guided segmentation method.

Determining collateral information describing blood flow in collaterals of a blood vessel system in a target region of a patient from a four-dimensional vascular data set describing image values of temporal flow of a contrast medium and/or marked blood constituents as recorded by a medical imaging device is provided. A method includes segmenting the blood vessel system in the vascular data set and determining collaterals among the segmented blood vessels by a collateral classifier. For all collaterals determined, a diameter of the collateral is determined taking into account the segmentation, a filling parameter describing the filling of the collaterals, and a time parameter describing the time response relative to a reference point in the blood vessel system from a temporal course of the image values in a portion of the collaterals under consideration. The method includes determining the collateral information from the diameter, the filling parameter, and the time parameter.

The disclosure relates to methods, devices, systems, and computer storage medium for performing medical image segmentation. The method includes: The method includes: obtaining, by a device, a first medical image and a second medical image with a labeled region; performing, by the device, feature extraction on the first medical image and the second medical image respectively, to obtain first feature information of the first medical image and second feature information of the second medical image; obtaining, by the device, optical flow motion information from the second medical image to the first medical image according to the first feature information and the second feature information; and segmenting, by the device, the first medical image according to the optical flow motion information and the labeled region, to obtain a segmentation result of the first medical image.

A platform is provided for generating 3D models of a tumor segmented from a series of 2D medical images and for identifying from these 3D models, radiomic features that may be used for diagnostic, prognostic, and treatment response assessment of the tumor. The radiomic features may be shape-based features, intensity-based features, textural features, and filter-based features. The radiomic features are compared to remove sufficiently redundant features, thereby producing a reduced set of radiomic features, which is then compared to separate genomic data and/or outcome data to identify clinically and biologically significant radiomic features for diagnostic, prognostic, and treatment response assessment, other applications.

A first extracting unit extracts at least one portion of a region of a target structure that includes lumen structures having branches from a three dimensional image of the target structure. A second extracting unit extracts the lumen structures from the at least one portion of the region of the target region. An index value calculating unit calculates an index value that represents the uniformity of the distribution of the lumen structures within the at least one portion of the region of the target structure, based on the at least one portion of the region of the target structure and the extracted lumen structures. Further, a quantifying unit quantifies the degree of reliability of the image quality of the at least one portion of the region of the target structure.