Examples of methods for model prediction are described herein. In some examples, a method includes predicting a compensated model. In some examples, the compensated model is predicted based on a three-dimensional (3D) object model. In some examples, a method includes predicting a deformed model. In some examples, the deformed mode is predicted based on the compensated model.

Examples of methods for model prediction are described herein. In some examples, a method includes predicting a compensated model. In some examples, the compensated model is predicted based on a three-dimensional (3D) object model. In some examples, a method includes predicting a deformed model. In some examples, the deformed mode is predicted based on the compensated model.

A learning-based active surface model for medical image segmentation uses a method including: (a) data generation: obtaining medical images and associated ground truths, and splitting the sample images into a training set and a testing set; (b) raw segmentation: constructing a surface initialization network, parameters of the network trained by images and labels in the training set; (c) surface initialization: segmenting the images by the surface initialization network, and generating the point cloud data as the initial surface from the segmentation; (d) fine segmentation: constructing the surface evolution network, the parameters of the network trained by the initial surface obtained in step (c); (e) surface evolution: deforming the initial surface points along the offsets to obtain the predicted surface, the offsets presenting the prediction of the surface evolution network; (f) surface reconstruction: reconstructing the 3D volumes from the set of predicted surface points set to obtain the final segmentation results.

A tree crown extraction method based on UAV multi-source remote sensing includes: obtaining a visible light image and LIDAR point clouds, taking a digital orthophoto map (DOM) and the LIDAR point clouds as data sources, using a method of watershed segmentation and object-oriented multi-scale segmentation to extract single tree crown information under different canopy densities. The object-oriented multi-scale segmentation method is used to extract crown and non-crown areas, and a tree crown distribution range is extracted with the crown area as a mask; a preliminary segmentation result of single tree crown is obtained by the watershed segmentation method based on a canopy height model; a brightness value of DOM is taken as a feature, the crown area of the DOM is performed secondary segmentation based on a crown boundary to obtain an optimized single tree crown boundary information, which greatly increases the accuracy of remote sensing tree crown extraction.

A tree crown extraction method based on UAV multi-source remote sensing includes: obtaining a visible light image and LIDAR point clouds, taking a digital orthophoto map (DOM) and the LIDAR point clouds as data sources, using a method of watershed segmentation and object-oriented multi-scale segmentation to extract single tree crown information under different canopy densities. The object-oriented multi-scale segmentation method is used to extract crown and non-crown areas, and a tree crown distribution range is extracted with the crown area as a mask; a preliminary segmentation result of single tree crown is obtained by the watershed segmentation method based on a canopy height model; a brightness value of DOM is taken as a feature, the crown area of the DOM is performed secondary segmentation based on a crown boundary to obtain an optimized single tree crown boundary information, which greatly increases the accuracy of remote sensing tree crown extraction.

A contour detection apparatus includes at least one processor. The processor detects a first nail contour defining a nail region from a finger image of a finger including a nail by performing fitting with a nail contour model. Further, the processor obtains a second nail contour input from a user against the first nail contour that the user does not approve. Further, the processor classifies the first nail contour as a group based on dimensional information on dimensions of the first nail contour, and derives difference information indicating a difference between the first nail contour and the second nail contour.

A contour detection apparatus includes at least one processor. The processor detects a first nail contour defining a nail region from a finger image of a finger including a nail by performing fitting with a nail contour model. Further, the processor obtains a second nail contour input from a user against the first nail contour that the user does not approve. Further, the processor classifies the first nail contour as a group based on dimensional information on dimensions of the first nail contour, and derives difference information indicating a difference between the first nail contour and the second nail contour.

A system and method is provided for generating a finite element (FE) model of an anatomical structure based on a fitted model (340) of the anatomical structure and association data. A segmentation model (310) may be provided for segmenting the anatomical structure. Association data may be obtained which associates a segmentation model part (315) of the segmentation model (310) with a mesh property, the segmentation model part (315) representing a pre-determined anatomical region of interest. The segmentation model may be applied to a medical image (320) of a subject, thereby obtaining a fitted model (340) providing a segmentation of the anatomical structure (330). The finite element model (350) may then be generated based on the fitted model (340) and the association data, said generating comprising meshing a finite element model part of the finite element model in accordance with the mesh property, the finite element model part corresponding with the pre-determined anatomical region of interest. Advantageously, this may result in an efficient generation of the FE model needing fewer manual iterations and/or alterations in the model or in the mesh.

The application discloses a method and system for segmenting a lung image. The method may include obtaining a target image relating to a lung region. The target image may include a plurality of image slices. The method may also include segmenting the lung region from the target image, identifying an airway structure relating to the lung region, and identifying one or more fissures in the lung region. The method may further include determining one or more pulmonary lobes in the lung region.

A photoacoustic image evaluation apparatus includes a processor configured to acquire a first photoacoustic image generated at a first point in time and a second photoacoustic image generated at a second point in time before the first point in time, the first and second photoacoustic images being photoacoustic images generated by detecting photoacoustic waves generated inside a subject, who has been subjected to blood vessel regeneration treatment, by emission of light into the subject; acquire a blood vessel regeneration index, which indicates a state of a blood vessel by the regeneration treatment, based on a difference between a blood vessel included in the first photoacoustic image and a blood vessel included in the second photoacoustic image; and display the blood vessel regeneration index on a display.