Endoscopic video texture mapping on pre-built 3-D anatomical objects without camera tracking.
Traditional minimally invasive surgeries use a view port provided by an endoscope or laparoscope. We argue that a useful addition to typical endoscopic imagery would be a global 3-D view providing a wider field of view with explicit depth information for both the exterior and interior of target anatomy. One technical challenge of implementing such a view is finding efficient and accurate means of registering texture images from the laparoscope on prebuilt 3-D surface models of target anatomy derived from magnetic resonance (MR) or computed tomography (CT) images. This paper presents a novel method for addressing this challenge that differs from previous approaches, which depend on tracking the position of the laparoscope. We take advantage of the fact that neighboring frames within a video sequence usually contain enough coherence to allow a 2-D-2-D registration, which is a much more tractable problem. The texturing process can be bootstrapped by an initial 2-D-3-D user-assisted registration of the first video frame followed by mostly-automatic texturing of subsequent frames. We perform experiments on phantom and real data, validate the algorithm against the ground truth, and compare it with the traditional tracking method by simulations. Experiments show that our method improves registration performance compared to the traditional tracking approach.
Wang
Xianwang
X
Center of Visualization and Virtual Environment (CVVE), University of Kentucky, Lexington, KY 40507, USA. xwang@cs.uky.edu
Zhang
Qing
Q
Han
Qiong
Q
Yang
Ruigang
R
Carswell
Melody
M
Seales
Brent
B
Sutton
Erica
E
D000465:Algorithms; D053704:Capsule Endoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique