Topology-Constrained Learning for Efficient Laparoscopic Liver Landmark Detection
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
Liver landmarks provide crucial anatomical guidance to the surgeon during
laparoscopic liver surgery to minimize surgical risk. However, the tubular
structural properties of landmarks and dynamic intraoperative deformations pose
significant challenges for automatic landmark detection. In this study, we
introduce TopoNet, a novel topology-constrained learning framework for
laparoscopic liver landmark detection. Our framework adopts a snake-CNN
dual-path encoder to simultaneously capture detailed RGB texture information
and depth-informed topological structures. Meanwhile, we propose a
boundary-aware topology fusion (BTF) module, which adaptively merges RGB-D
features to enhance edge perception while preserving global topology.
Additionally, a topological constraint loss function is embedded, which
contains a center-line constraint loss and a topological persistence loss to
ensure homotopy equivalence between predictions and labels. Extensive
experiments on L3D and P2ILF datasets demonstrate that TopoNet achieves
outstanding accuracy and computational complexity, highlighting the potential
for clinical applications in laparoscopic liver surgery. Our code will be
available at https://github.com/cuiruize/TopoNet.
