SutureFormer: 通过像素空间中的目标引导离线强化学习学习手术轨迹
SutureFormer: Learning Surgical Trajectories via Goal-conditioned Offline RL in Pixel Space
AI总结：
本文提出SutureFormer，一种基于目标引导的离线强化学习框架，通过稀疏标注到密集奖励信号的插值，有效学习手术针轨迹预测，减少平均位移误差58.6%。
摘要：
从内窥镜视频预测手术针轨迹对于机器人辅助缝合至关重要，能够实现预见性规划、实时引导和更安全的运动执行。现有直接从视觉观测学习运动分布的方法往往忽视相邻运动步骤之间的序列依赖性。此外，稀疏路径点标注通常无法提供足够的监督，进一步增加了监督或模仿学习方法的难度。为了解决这些挑战，我们将基于图像的针轨迹预测 formulations 为一个序列决策问题，在其中将针尖视为一个在像素空间中逐步移动的智能体。这种 formulation 自然捕捉了针运动的连续性，并能够显式建模在时间上物理上合理的像素级状态转换。从这个角度来看，我们提出SutureFormer，一种目标引导的离线强化学习框架，通过三次样条插值将稀疏标注转换为密集奖励信号，鼓励策略在利用有限专家指导的同时探索合理的未来运动路径。SutureFormer 使用观察编码器编码可变长度片段，以捕捉局部空间线索和长距离时间动态，并通过由离散方向和连续幅度组成的操作自回归地预测未来路径点。为了实现从专家演示中稳定离线策略优化，我们采用保守Q学习与行为克隆正则化。在包含1,158条轨迹的新的肾伤口缝合数据集中进行的实验表明，与最强基线相比，SutureFormer将平均位移误差减少了58.6%，证明了将针轨迹预测建模为像素级序列动作学习的有效性。
英文摘要：
Predicting surgical needle trajectories from endoscopic video is critical for robot-assisted suturing, enabling anticipatory planning, real-time guidance, and safer motion execution. Existing methods that directly learn motion distributions from visual observations tend to overlook the sequential dependency among adjacent motion steps. Moreover, sparse waypoint annotations often fail to provide sufficient supervision, further increasing the difficulty of supervised or imitation learning methods. To address these challenges, we formulate image-based needle trajectory prediction as a sequential decision-making problem, in which the needle tip is treated as an agent that moves step by step in pixel space. This formulation naturally captures the continuity of needle motion and enables the explicit modeling of physically plausible pixel-wise state transitions over time. From this perspective, we propose SutureFormer, a goal-conditioned offline reinforcement learning framework that leverages sparse annotations to dense reward signals via cubic spline interpolation, encouraging the policy to exploit limited expert guidance while exploring plausible future motion paths. SutureFormer encodes variable-length clips using an observation encoder to capture both local spatial cues and long-range temporal dynamics, and autoregressively predicts future waypoints through actions composed of discrete directions and continuous magnitudes. To enable stable offline policy optimization from expert demonstrations, we adopt Conservative Q-Learning with Behavioral Cloning regularization. Experiments on a new kidney wound suturing dataset containing 1,158 trajectories from 50 patients show that SutureFormer reduces Average Displacement Error by 58.6% compared with the strongest baseline, demonstrating the effectiveness of modeling needle trajectory prediction as pixel-level sequential action learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
