The University of North Carolina at Chapel Hill

Siemens Healthcare Gmbh

Rui Wang

Jan-Michael Frahm

Stephen Murray Pizer

Ruibin Ma

Sarah Kelly McGill

Julian Gary Rosenman

Vivek Kumar Singh

Yao-Jen Chang

Birgi Tamersoy

James True Price

Miao Fan

Qingyu Zhao

For x-ray detector pose estimation, a machine-learned model is used to estimate locations of markers, including occluded or other non-visible markers, from an image. The locations of the markers, including the non-visible markers are used to determine the pose of the X-ray detector for aligning an X-ray tube with the X-ray detector.

X-ray detector pose estimation in medical imaging

Methods, systems, and computer readable media for deriving a three-dimensional (3D) textured surface from endoscopic video are disclosed. According to one method for deriving a 3D textured surface from endoscopic video, the method comprises: performing video frame preprocessing to identify a plurality of video frames of an endoscopic video, wherein the video frame preprocessing includes informative frame selection, specularity removal, and key-frame selection; generating, using a neural network or a shape-from-motion-and-shading (SfMS) approach, a 3D textured surface from the plurality of video frames; and optionally registering the 3D textured surface to at least one CT image.

Methods, systems, and computer readable media for deriving a three-dimensional (3D) textured surface from endoscopic video

Methods, systems, and computer readable media for deriving a three-dimensional (3D) surface from colonoscopic video are disclosed. According to one method for deriving a 3D surface from colonoscopic video, the method comprises: performing video frame preprocessing to identify a plurality of keyframes of a colonoscopic video, wherein the video frame preprocessing includes informative frame selection and keyframe selection; generating, using a recurrent neural network and direct sparse odometry, camera poses and depth maps for the keyframes; and fusing, using SurfelMeshing and the camera poses, the depth maps into a three-dimensional (3D) surface of a colon portion, wherein the 3D surface indicates at least one region of the colon portion that was not visualized.

Methods, systems, and computer readable media for three-dimensional (3D) reconstruction of colonoscopic surfaces for determining missing regions

Growing community of inventors

Chapel Hill, NC, United States of America

Rui Wang