Laparoscopic liver surgery poses a complex intraoperative dynamic environment
for surgeons, where remains a significant challenge to distinguish critical or
even hidden structures inside the liver. Liver anatomical landmarks, e.g.,
ridge and ligament, serve as important markers for 2D-3D alignment, which can
significantly enhance the spatial perception of surgeons for precise surgery.
To facilitate the detection of laparoscopic liver landmarks, we collect a novel
dataset called L3D, which comprises 1,152 frames with elaborated landmark
annotations from surgical videos of 39 patients across two medical sites. For
benchmarking purposes, 12 mainstream detection methods are selected and
comprehensively evaluated on L3D. Further, we propose a depth-driven geometric
prompt learning network, namely D2GPLand. Specifically, we design a Depth-aware
Prompt Embedding (DPE) module that is guided by self-supervised prompts and
generates semantically relevant geometric information with the benefit of
global depth cues extracted from SAM-based features. Additionally, a
Semantic-specific Geometric Augmentation (SGA) scheme is introduced to
efficiently merge RGB-D spatial and geometric information through reverse
anatomic perception. The experimental results indicate that D2GPLand obtains
state-of-the-art performance on L3D, with 63.52% DICE and 48.68% IoU scores.
Together with 2D-3D fusion technology, our method can directly provide the
surgeon with intuitive guidance information in laparoscopic scenarios.

为了在腹腔镜肝脏手术中提供直观指导信息，本文提出了一种基于深度驱动几何提示学习网络的方法（D2GPLand），该方法结合二维 - 三维融合技术，在 L3D 数据集上取得了最新的性能，具有 63.52％的 DICE 和 48.68％的 IoU 得分。