Advanced surgical robotics has made robot-assisted endoscopic submucosal dissection (ESD) a promising approach for the en-bloc resection of large lesions, with the potential to reduce recurrence and improve long-term outcomes. However, the technical complexity and risk of complications in ESD demand stable and precise visual guidance to maintain an accurate dissection corridor and a safe tissue margin. Dense confidence fields provide an effective representation for this purpose by describing both the preferred dissection region and its spatial transition to surrounding tissue. However, reliable confidence field estimation remains challenging in dynamic endoscopic scenes due to smoke, specular highlights, tissue deformation, weak texture, and the thin geometric structure of the target region. To address these challenges, we formulate dissection guidance as a geometry-aware confidence field estimation problem and propose GeoCFNet, a geometry-aware confidence field network built on a pretrained DINOv3 backbone. GeoCFNet integrates a Token-Differentiated Fusion module to aggregate class-token context with dense patch representations, a SegFormer decoder for confidence regression, and Geometry-Aware Spatial Regularization (GASR) to preserve spatial coherence and local geometric transitions. Experimental results show that GeoCFNet achieves RMSE 0.0480, PSNR 27.1995, SSIM 0.3397, and CC 0.2466, indicating accurate and geometrically stable confidence field estimation for robot-assisted ESD guidance.

翻译：先进的手术机器人技术使得机器人辅助内镜黏膜下剥离术（ESD）成为大病变整块切除的一种有前景的方法，具有降低复发率并改善远期结局的潜力。然而，ESD的技术复杂性和并发症风险要求稳定且精确的视觉引导，以维持准确的剥离通道和安全的组织边缘。密集置信场通过描述优先剥离区域及其向周围组织的空间过渡，为此提供了有效的表征方式。然而，在动态内镜场景中，由于烟雾、镜面高光、组织变形、弱纹理以及目标区域的薄几何结构，可靠的置信场估计仍具挑战。为应对这些挑战，我们将剥离引导问题形式化为几何感知的置信场估计问题，并提出了GeoCFNet——一种基于预训练DINOv3骨干网络的几何感知置信场网络。GeoCFNet集成了令牌差异融合模块以聚合类别令牌上下文与密集块表征、用于置信回归的SegFormer解码器，以及几何感知空间正则化（GASR）以保持空间一致性和局部几何过渡。实验结果表明，GeoCFNet实现了RMSE 0.0480、PSNR 27.1995、SSIM 0.3397和CC 0.2466，表明其能为机器人辅助ESD引导提供精确且几何稳定的置信场估计。