Abstract: Implementations disclose methods and systems for rendering, by a browser, a content item projected on a mesh. A method includes providing, by a browser executing on a user device, an application programming interface (API) to communicate with a web application that includes a user interface to present a content item on the user device; receiving, by the browser from the web application via the API, an instruction to project the content item on a mesh, where the instruction identifies the content item and the mesh; rendering, by the browser and without involvement of the web application, a first frame of the content item projected on the mesh in a first orientation; and causing, by the browser, the rendered first frame to be displayed on the user device.

Abstract: Implementations disclose methods and systems for rendering, by a browser, a content item projected on a mesh. A method includes providing, by a browser executing on a user device, an application programming interface (API) to communicate with a web application that includes a user interface to present a content item on the user device; receiving, by the browser from the web application via the API, an instruction to project the content item on a mesh, where the instruction identifies the content item and the mesh; rendering, by the browser and without involvement of the web application, a first frame of the content item projected on the mesh in a first orientation; and causing, by the browser, the rendered first frame to be displayed on the user device.

Abstract: A method, apparatus and computer program product are hereby provided to detect anatomical elements in a medical image. In this regard, the method, apparatus, and computer program product may receive a test image and generate a classified image by applying an image classifier to the test image. The image classifier may include at least one decision tree for evaluating at least one pixel value of the test image and the classified image may include a plurality of pixel values. Each pixel value may be associated with a probability that an anatomical element is located at the pixel location. The method, apparatus, and computer program product may also evaluate the classified image using an anatomical model to detect at least one anatomical element within the classified image.

Abstract: Methods and systems for interactively segmenting 3D image data are provided. An initial segmentation of the 3D image data is obtained, and for each of a plurality of image regions, a segmentation uncertainty indicator for the initial image segmentation is associated with the image region and a strength is assigned to the segmentation uncertainty indicator. A low-confidence region in the 3D image data is identified based at least in part on proximity of the low-confidence region to the image regions and strengths of the corresponding segmentation uncertainty indicators. An optimization routine may be applied to an objective function whose value depends at least in part on proximity of the candidate region to the image regions and the strengths of the corresponding uncertainty indicators to identify the low-confidence region from among a plurality of candidate regions.

Abstract: A method, apparatus and computer program product are hereby provided to detect anatomical elements in a medical image. In this regard, the method, apparatus, and computer program product may receive a test image and generate a classified image by applying an image classifier to the test image. The image classifier may include at least one decision tree for evaluating at least one pixel value of the test image and the classified image may include a plurality of pixel values. Each pixel value may be associated with a probability that an anatomical element is located at the pixel location. The method, apparatus, and computer program product may also evaluate the classified image using an anatomical model to detect at least one anatomical element within the classified image.

Abstract: Methods and systems for interactively segmenting 3D image data are provided. An initial segmentation of the 3D image data is obtained, and for each of a plurality of image regions, a segmentation uncertainty indicator for the initial image segmentation is associated with the image region and a strength is assigned to the segmentation uncertainty indicator. A low-confidence region in the 3D image data is identified based at least in part on proximity of the low-confidence region to the image regions and strengths of the corresponding segmentation uncertainty indicators. An optimization routine may be applied to an objective function whose value depends at least in part on proximity of the candidate region to the image regions and the strengths of the corresponding uncertainty indicators to identify the low-confidence region from among a plurality of candidate regions.