Systems and methods for articular cartilage thickness mapping and lesion quantification operate on 3D medical image data to reconstruct cartilage surfaces, estimate surface normals, determine cartilage thickness, and identify regions of full-thickness cartilage loss (FCL). Reconstructed cartilage surfaces can be parcellated into subregions using a rule-based approach.

A local extension method for segmentation of anatomical treelike structures includes receiving an initial segmentation of 3D image data including an initial treelike structure. A target point in the 3D image data is defined, and a region of interest based on the target point is extracted to create a sub-image. Highly tubular voxels are detected in the sub-image, and a spillage-constrained region growing is performed using the highly tubular voxels as seed points. Connected components are extracted from the results of the region growing. The extracted components are pruned to discard components not likely to be connected to the initial treelike structure, keeping only candidate components likely to be a valid sub-tree of the initial treelike structure. The candidate components are connected to the initial treelike structure, thereby extending the initial segmentation in the region of interest.

A local extension method for segmentation of anatomical treelike structures includes receiving an initial segmentation of 3D image data including an initial treelike structure. A target point in the 3D image data is defined, and a region of interest based on the target point is extracted to create a sub-image. Highly tubular voxels are detected in the sub-image, and a spillage-constrained region growing is performed using the highly tubular voxels as seed points. Connected components are extracted from the results of the region growing. The extracted components are pruned to discard components not likely to be connected to the initial treelike structure, keeping only candidate components likely to be a valid sub-tree of the initial treelike structure. The candidate components are connected to the initial treelike structure, thereby extending the initial segmentation in the region of interest.

Systems and methods for generating and processing images captured while inspecting above-ground pipelines are disclosed. Embodiments may include a robotic crawler or other devices which carry imaging equipment and traverse a target pipe which are configured to capture image data simultaneously from a plurality of angles. Such systems may substantially reduce and in some cases overcome the need to take multiple traversals of a pipeline under inspection. Embodiments may also be directed toward control systems for such devices as well as image processing systems which process the multiple image sets to produce a composite imaging result.

Systems and methods for generating and processing images captured while inspecting above-ground pipelines are disclosed. Embodiments may include a robotic crawler or other devices which carry imaging equipment and traverse a target pipe which are configured to capture image data simultaneously from a plurality of angles. Such systems may substantially reduce and in some cases overcome the need to take multiple traversals of a pipeline under inspection. Embodiments may also be directed toward control systems for such devices as well as image processing systems which process the multiple image sets to produce a composite imaging result.

Provided is an information processing apparatus including an information acquisition section (104) that acquires information of a first region (700) specified by a filling input operation on image data (610) of a living tissue by a user, and a region determination section (108) that executes fitting on a boundary of the first region on the basis of the image data and information of the first region and determines a second region (702) to be subjected to predetermined processing.

Provided is an information processing apparatus including an information acquisition section (104) that acquires information of a first region (700) specified by a filling input operation on image data (610) of a living tissue by a user, and a region determination section (108) that executes fitting on a boundary of the first region on the basis of the image data and information of the first region and determines a second region (702) to be subjected to predetermined processing.

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

A localization and mapping system and method for a motor vehicle is disclosed and includes at least one camera configured to obtain images of an environment surrounding the motor vehicle, at least one sensor configured to obtain location information for objects surrounding the motor vehicle and a controller configured to receive the images captured by the at least one camera and the location information obtained by the at least one sensor. The controller enhances the captured images utilizing a neural network and combines the enhanced images with the location information to localize the vehicle within the mapped environment.