An image segmentation method is disclosed that allows a user to select image component types, for example tissue types and or background, and have the method of the present invention segment the image according to the user's input utilizing the superpixel image feature data and spatial relationships.

Some embodiments are directed to a segmentation of medical images. For example, a medical image may be registering to multiple atlas images after which a segmentation function may be applied. Multiple segmentation may be fused into a final overall segmentation. The atlas images may be selected on the basis of high segmentation quality or low registration quality.

Some embodiments are directed to a segmentation of medical images. For example, a medical image may be registering to multiple atlas images after which a segmentation function may be applied. Multiple segmentation may be fused into a final overall segmentation. The atlas images may be selected on the basis of high segmentation quality or low registration quality.

Embodiments of the present disclosure are related to providing a method and device processing a first set of volumetric image data comprising cross-sectional images of a myocardium and displaying a second set of volumetric image data of the myocardium. A curved plane to rectangular plane transformation of cross-sectional images of myocardium of human heart is proposed. After the transformation, a combined and reconstructed set of myocardium images are superimposed with a modified Bull's Eye View (BEV) map and corresponding parameters indicating extent of fibrosis to obtain a second set of volumetric image data of myocardium. In addition to quantifying and displaying the extent of fibrosis, the proposed solution preserves neighborhood and adjacency criteria of abnormal tissues of myocardium walls of human heart.

Embodiments of the present disclosure are related to providing a method and device processing a first set of volumetric image data comprising cross-sectional images of a myocardium and displaying a second set of volumetric image data of the myocardium. A curved plane to rectangular plane transformation of cross-sectional images of myocardium of human heart is proposed. After the transformation, a combined and reconstructed set of myocardium images are superimposed with a modified Bull's Eye View (BEV) map and corresponding parameters indicating extent of fibrosis to obtain a second set of volumetric image data of myocardium. In addition to quantifying and displaying the extent of fibrosis, the proposed solution preserves neighborhood and adjacency criteria of abnormal tissues of myocardium walls of human heart.

Systems and methods are provided in which intraoperatively acquired surface data is employed to verify the correspondence of an intraoperatively selected spinal level with a spinal level that is pre-selected based on volumetric image data. Segmented surface data corresponding to the pre-selected spinal levels may be obtained from the volumetric image data, such that the segmented surface data corresponds to a spinal segment that is expected to be exposed and identified intraoperatively during the surgical procedure. The segmented surface data from the pre-selected spinal level, and adjacent segmented surface data from an adjacent spinal level that is adjacent to the pre-selected spinal level, is registered to the intraoperative surface data, and quality measures associated with the registration are obtained, thereby permitting an assessment or a determination of whether or not the pre-selected spinal surface (in the volumetric frame or reference) is likely to correspond to the intraoperatively selected spinal level.

Systems and methods are provided in which intraoperatively acquired surface data is employed to verify the correspondence of an intraoperatively selected spinal level with a spinal level that is pre-selected based on volumetric image data. Segmented surface data corresponding to the pre-selected spinal levels may be obtained from the volumetric image data, such that the segmented surface data corresponds to a spinal segment that is expected to be exposed and identified intraoperatively during the surgical procedure. The segmented surface data from the pre-selected spinal level, and adjacent segmented surface data from an adjacent spinal level that is adjacent to the pre-selected spinal level, is registered to the intraoperative surface data, and quality measures associated with the registration are obtained, thereby permitting an assessment or a determination of whether or not the pre-selected spinal surface (in the volumetric frame or reference) is likely to correspond to the intraoperatively selected spinal level.

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.

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 system for separating audio based on sound producing objects includes a processor configured to receive video data and audio data. The processor is also configured to perform object detection using the video data to identify a number of sound producing objects in the video data and predict a separation for each sound producing object detected in the video data. The processor is also configured to generate separated audio data for each sound producing object using the separation and the audio data.