用拓扑约束提升腹腔镜肝脏关键点检测精度与效率
Topology-Constrained Learning for Efficient Laparoscopic Liver Landmark Detection
- 设计双路径编码器融合颜色与深度信息，捕捉细节纹理与结构拓扑
- 在两个数据集上达到领先性能，计算开销低适合临床部署
- 适用于需要高精度三维定位的微创手术辅助系统
肝脏关键点为腹腔镜肝手术提供重要解剖参考，有助于降低手术风险。然而，关键点的管状结构特性及术中动态形变给自动检测带来挑战。本文提出TopoNet，一种新型拓扑约束学习框架用于腹腔镜肝脏关键点检测。该框架采用蛇形-卷积双路径编码器，同时捕获精细的RGB纹理信息与深度引导的拓扑结构。此外，提出边界感知拓扑融合（BTF）模块，自适应融合RGB-D特征，增强边缘感知并保持全局拓扑一致性。还引入拓扑约束损失函数，包含中心线约束损失与拓扑持久性损失，确保预测结果与标注间同伦等价。在L3D与P2ILF数据集上的大量实验表明，TopoNet在准确率与计算复杂度方面表现优异，展现出在腹腔镜肝手术中的临床应用潜力。代码将公开于https://github.com/cuiruize/TopoNet。
原文摘要 · Abstract (English)
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.
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