BCRNet: Enhancing Landmark Detection in Laparoscopic Liver Surgery via Bezier Curve Refinement
Journal:
arXiv
Published Date:
Jun 18, 2025
Abstract
Laparoscopic liver surgery, while minimally invasive, poses significant
challenges in accurately identifying critical anatomical structures. Augmented
reality (AR) systems, integrating MRI/CT with laparoscopic images based on
2D-3D registration, offer a promising solution for enhancing surgical
navigation. A vital aspect of the registration progress is the precise
detection of curvilinear anatomical landmarks in laparoscopic images. In this
paper, we propose BCRNet (Bezier Curve Refinement Net), a novel framework that
significantly enhances landmark detection in laparoscopic liver surgery
primarily via the Bezier curve refinement strategy. The framework starts with a
Multi-modal Feature Extraction (MFE) module designed to robustly capture
semantic features. Then we propose Adaptive Curve Proposal Initialization
(ACPI) to generate pixel-aligned Bezier curves and confidence scores for
reliable initial proposals. Additionally, we design the Hierarchical Curve
Refinement (HCR) mechanism to enhance these proposals iteratively through a
multi-stage process, capturing fine-grained contextual details from multi-scale
pixel-level features for precise Bezier curve adjustment. Extensive evaluations
on the L3D and P2ILF datasets demonstrate that BCRNet outperforms
state-of-the-art methods, achieving significant performance improvements. Code
will be available.
