An accurate, straightforward computer vision algorithm for optimal tumor-feeding visualization in cone-beam computed tomography hepatic arteriography: A preliminary study.

[AIM] Although standardized 3D volume rendering techniques (VRT) and embolization guidance visualize and identify tumor-feeding arteries, current vessel tracking software lacks automatic angle recommendations. This forces an operator, e.g. an interventional radiologist, to leave an ongoing procedure to manually manipulate the system and find the best angle for each feeding vessel-requiring time-consuming re-scrubbing. We propose a computer vision algorithm that suggests a rotation/angle in the VRT where a tumor-feeding artery's view is maximized. We focus on hepatocellular carcinoma.

[METHODS] Our algorithm accepts a series of post-embolization guidance frames extracted from the 3D VRT; the VRT is rotated in 5° intervals from, e.g., ±15°, fixing one axis (e.g. CRAN/CAUD) and rotating the other (e.g. LAO/RAO). Our algorithm segments the embolization guidance line and recommends 4 views/angles by maximizing the features of line length (contour area) and convex hull area. We developed/iterated our algorithm using 19 patient cases and feedback from various experts.

[RESULTS] Over a 50-patient internal validation set, according to an interventional radiologist with 33 years of experience, a view/angle sufficient for the embolization task was always present among the top-4 views/angles suggested by our algorithm (100% retrieval relevance).

[CONCLUSION] Sufficient view/angle selection for hepatic artery embolization can be achieved using traditional computer vision. Our technique is much faster and more explainable than deep learning approaches, and could greatly improve radiologists' procedural efficiency. We recommend conducting a larger study with more patients and further technical iteration.