2D Ultrasound to 3D CT Image Registration for Liver Ablation Surgery

Minimally invasive liver cancer treatments are increasingly favoured for their reduced risks and improved patient outcomes. However, real-time guidance during liver ablation remains challenging due to limited resources and the reliance on ultrasound (US) imaging. This project introduces a deep learning-based approach for 2D US to 3D Computed Tomography (CT) image registration, eliminating the need for 3D tracking systems. By integrating self-supervised feature embeddings and matching point estimation methods, our approach enhances real-time visualization of the liver, improving surgical precision and cost-effectiveness. With around 0.99 Normalized Cross-Correlation (NCC) value and 25.48-pixel distance in US and CT image registration, this research promises significant advancements in liver tumor ablation procedures and holds potential for broader applications in other ablation therapies.