通过识别组织张力评估手术技能，突破传统视频分析局限。
Beyond Instrument Motion: Recognizing Tissue Tension Toward Surgical Skill Assessment
- 基于稀疏点轨迹建模组织张力，轻量化设计提升效率
- 在首个专家标注的SurgTension数据集上达到媲美大模型的表现
- 适用于腹腔镜和机器人直肠癌手术技能客观评估
微创外科手术绩效评估主要依赖人工专家评审，耗时、主观且难以扩展。现有手术视频理解方法虽能实现器械分割、手术阶段识别和动作识别，但未明确捕捉精细的组织操作这一关键手术质量指标。为此，我们提出组织张力识别这一新的临床驱动任务，针对腹腔镜与机器人辅助直肠癌手术。为支持该任务，我们构建了SurgTension——首个由专家标注的组织张力数据集，提供客观评估基准。我们进一步提出TensionTRAC，一种轻量级轨迹建模框架，通过紧凑的轨迹编码器从稀疏点轨迹中推断组织张力，在性能上可媲美强预训练视频主干模型。
原文摘要 · Abstract (English)
Surgical performance assessment in minimally invasive surgery largely relies on manual expert review, making it time-consuming, subjective, and difficult to scale. While existing surgical video understanding methods address tasks such as instrument segmentation, surgical phase recognition, and action recognition, they do not explicitly capture fine-grained tissue handling, a key indicator of surgical quality. To address this gap, we introduce tissue tension recognition, a new clinically motivated video understanding task for laparoscopic and robot-assisted rectal cancer surgery. To support this task, we construct SurgTension, the first expert-annotated tissue tension dataset, providing a benchmark for objective tissue tension recognition. We further propose TensionTRAC, a lightweight trajectory-based framework that models tissue tension from sparse point trajectories. Using a compact trajectory encoder, TensionTRAC achieves competitive performance against strong pretrained video backbones.
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