SCISSR：基于涂鸦的交互式手术分割与细化
SCISSR: Scribble-Conditioned Interactive Surgical Segmentation and Refinement
AI总结：
SCISSR通过轻量级涂鸦编码器将自由手绘涂鸦转换为密集提示嵌入，实现手术场景分割的交互式迭代细化，优于点提示方法。
摘要：
准确分割手术场景中的组织和器械因不规则形状、细长结构、反光和频繁遮挡而注释密集。虽然SAM模型支持点、框和掩码提示，但点提示过于稀疏，框提示过于粗糙，难以定位这些挑战性目标。我们提出了SCISSR，一种可交互的手术场景分割框架。它引入了轻量级的涂鸦编码器，将自由手绘涂鸦转换为与掩码解码器兼容的密集提示嵌入，通过在错误区域绘制修正笔触实现目标对象的迭代细化。因为所有添加的模块（涂鸦编码器、空间门控融合和LoRA适配器）仅通过骨干网络的标准嵌入接口与骨干网络交互，该框架不局限于单一模型：本文基于SAM 2构建，但相同的组件可以转移到其他提示驱动的分割架构，如SAM 3，而无需结构修改。为保持预训练能力，我们仅训练这些轻量级添加模块，同时保持其余骨干网络冻结。在EndoVis 2018上的实验显示了强大的领域内性能，而在CholecSeg8k上的评估进一步证实了跨手术领域的鲁棒性。SCISSR在五轮交互中达到95.41%的Dice系数，在三轮交互中达到96.30%的Dice系数，优于两个基准的迭代点提示方法。
英文摘要：
Accurate segmentation of tissues and instruments in surgical scenes is annotation-intensive due to irregular shapes, thin structures, specularities, and frequent occlusions. While SAM models support point, box, and mask prompts, points are often too sparse and boxes too coarse to localize such challenging targets. We present SCISSR, a scribble-promptable framework for interactive surgical scene segmentation. It introduces a lightweight Scribble Encoder that converts freehand scribbles into dense prompt embeddings compatible with the mask decoder, enabling iterative refinement for a target object by drawing corrective strokes on error regions. Because all added modules (the Scribble Encoder, Spatial Gated Fusion, and LoRA adapters) interact with the backbone only through its standard embedding interfaces, the framework is not tied to a single model: we build on SAM 2 in this work, yet the same components transfer to other prompt-driven segmentation architectures such as SAM 3 without structural modification. To preserve pre-trained capabilities, we train only these lightweight additions while keeping the remaining backbone frozen. Experiments on EndoVis 2018 demonstrate strong in-domain performance, while evaluation on the out-of-distribution CholecSeg8k further confirms robustness across surgical domains. SCISSR achieves 95.41% Dice on EndoVis 2018 with five interaction rounds and 96.30% Dice on CholecSeg8k with three interaction rounds, outperforming iterative point prompting on both benchmarks.
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。
