Abstract: A method and system for calculating a volume of resected tissue from a stream of intraoperative images is disclosed. A stream of 2D/2.5D intraoperative images of resected tissue of a patient is received. The 2D/2.5D intraoperative images in the stream are acquired at different angles with respect to the resected tissue. A resected tissue surface is segmented in each of the 2D/2.5D intraoperative images. The segmented resected tissue surfaces are stitched to generate a 3D point cloud representation of the resected tissue surface. A 3D mesh representation of the resected tissue surface is generated from the 3D point cloud representation of the resected tissue surface. The volume of the resected tissue is calculated from the 3D mesh representation of the resected tissue surface.

Abstract: A computer-implemented method for providing a see-through visualization of a patient includes receiving an image dataset representative of anatomical features of the patient acquired using a medical image scanner and acquiring a body surface model of the patient using an RGB-D sensor. The body surface model is aligned with the image dataset in a canonical/common coordinate system to yield an aligned body surface model. A relative pose of a mobile device is determined with respect to the RGB-D sensor and a pose dependent visualization of the patient is created by rendering the image dataset at a viewpoint corresponding to the relative pose of the mobile device. Then, the pose dependent visualization of the patient may be presented on the mobile device.

Abstract: Intraoperative camera data is registered with medical scan data. The same salient features are located in both the medical scan data and the model from the camera data. The features are specifically labeled rather than just being represented by the data. At least an initial rigid registration is performed using the salient features. The coordinate systems of the camera and the medical scan data are aligned without external positions sensors for the intraoperative camera.

Abstract: A method for reconstructing 3-D vessel geometry of a vessel includes: receiving a plurality of 2-D rotational X-ray images of the vessel; extracting vessel centerline points for normal cross sections of each of the plurality of 2-D images; establishing a correspondence of the centerline points; constructing a 3-D centerline vessel tree skeleton of the vessel; constructing an initial 3-D vessel surface having a uniform radius normal to the 3-D centerline vessel tree skeleton; and constructing a target 3-D vessel surface by deforming the initial vessel surface to provide a reconstructed 3-D vessel geometry of the vessel.

Abstract: A database stores reference photographs of an assembly. The reference photographs are from different orientations relative to the assembly. By matching the query photograph to one or more of the reference photographs, the pose of the assembly in the query photograph is determined. Based on the pose, the pixels of the two-dimensional query photograph are related to a three-dimensional representation from engineering data. Using labeled parts from the engineering data, the parts represented in the query photograph are identified, and part information (e.g., segmentation, number, or other metadata) is provided relative to the query photograph.

Abstract: A method and system for cooperative diversity visual cognition in a wireless sensor network is disclosed. The method and system are capable of solving distributed visual cognition tasks (for example, online simultaneous reconstruction of 3D models of a large area) by using multiple video streams and exploiting cooperative diversity video sensing information while ensuring an optimal tradeoff between energy consumption and video quality of images received from said multiple video streams.

Abstract: A method and system for cooperative diversity visual cognition in a wireless sensor network is disclosed. The method and system are capable of solving distributed visual cognition tasks (for example, online simultaneous reconstruction of 3D models of a large area) by using multiple video streams and exploiting cooperative diversity video sensing information while ensuring an optimal tradeoff between energy consumption and video quality of images received from said multiple video streams.