SurgLAT: 深度認識ロボット腹腔鏡制御のための外科的潜在注意追跡
SurgLAT: Surgical Latent Attention Tracking for Depth-Aware Robotic Laparoscope Control
本論文は、動的な手術シーンにおける外科医の意図を潜在的な注意状態として追跡し、自律的な腹腔鏡カメラ制御を行うフレームワークSurgLATを提案する。凍結DINOv3エンコーダと状態条件付きトークンミキサーを用いて注意をモデル化し、RCM制約を考慮したロボット展開を実現する。
著者: Rulin Zhou, Qiujie Song, Yujie Ma, An Wang, Wanhao Liu, Guoheng Ma, Yidu Wang, Guankun Wang, Xingrong Diao, Jiankun Wang, Chaowei Zhu, Xianming Liu, Hongliang Ren
分類: cs.AI
原文アブストラクト
Autonomous laparoscopic camera control requires continuous understanding of the surgeon's operative intent in dynamic surgical scenes, where the target operative region is not a stable physical object but a latent and temporally evolving attention state. In this work, we present Surgical Latent Attention Tracking (SurgLAT), a causal online framework for latent surgical attention modeling and autonomous laparoscopic view control. SurgLAT uses a frozen DINOv3 encoder and a state-conditioned spatial token mixer to extract operative evidence under a memory-guided spatial prior, while a selective causal latent memory module jointly models short-term motion continuity and long-horizon surgical intent evolution through dynamic retrieval of current, recent, and historical latent states. The learned latent surgical attention state is decoded into a probabilistic attention heatmap and operative region for downstream endoscope guidance. Beyond perception, we further introduce a robotic deployment framework with explicit laparoscopic Remote Center of Motion (RCM) constrained control based on virtual-axis formulation, together with redundancy-aware null-space initialization for stable and smooth manipulator motion. We validate the full system on real laparoscopic surgical videos and a physical robotic laparoscope platform. Experimental results demonstrate robust online operative-region tracking and stable autonomous endoscopy adjustment under occlusion, rapid motion, and target transitions, highlighting the effectiveness of latent surgical intent modeling for surgical autonomy.