Intra-operative Tumor Tracking Using Optical Flow and Fluorescent Imaging
Image-guided surgery (IGS) can assist surgeons by modeling and visualizing objects of interest (tumors, nerves, etc.) that may be obstructed or difficult to recognize during surgery. Models based on pre-operative images are often not applicable during surgery because of motion and deformation. Therefore, real-time updates to IGS models are required. We propose an automated intra-operative tumor tracking system in which the initial tumor location is predicted using near infrared (NIR) fluorescence with indocyanine green (ICG), and the tumor is tracked using the Lucas-Kanade (LK) algorithm, a multi-resolution coarse-to-fine optical flow method. We simulate various conditions of intra-operative tumor movement, including movement speed and variations in image brightness. The LK method can accurately track tumors when speed of tumor movement and image brightness changes are low. However, when the speed of tumor movement increases or when image brightness changes by more than 30%, the LK method fails to track the tumor location. We compare the LK method to several other optical flow algorithms and find that LK has relatively high accuracy and tolerance in both speed and brightness changes, although each algorithm has strengths and weaknesses. In addition to the proposed intra-operative system, the simulations and metrics that we use in this study may serve as benchmarks to assess the performance of intra-operative tumor tracking algorithms.
The authors thank Chanchala Kaddi for reviewing and critiquing the manuscript. This work was supported in part by grants from National Institutes of Health (Center for Cancer Nanotechnology Excellence U54CA119338, and R01 CA163256), Georgia Cancer Coalition (Distinguished Cancer Scholar Award to Professor Wang).
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