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arXiv 2604.09151cs.CVnlin.PS
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基于机器人辅助手术的手术器械分割中CNN和Transformer模型的基准测试

Benchmarking CNN- and Transformer-Based Models for Surgical Instrument Segmentation in Robotic-Assisted Surgery

Sara Ameli

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AI总结：

本文通过SAR-RARP50数据集对比了五种深度学习模型在手术器械多类语义分割中的性能，发现DeepLabV3与SegFormer在复杂手术场景中表现优异，而Transformer架构进一步提升了泛化能力。

摘要：

准确分割机器人辅助手术中的手术器械对于实现上下文感知的计算机辅助干预至关重要，如工具跟踪、工作流分析和自主决策。本研究在SAR-RARP50数据集上评估了五种深度学习架构（UNet、UNet、DeepLabV3、Attention UNet和SegFormer）进行多类语义分割。模型使用结合交叉熵损失和Dice损失的复合损失函数来解决类别不平衡问题并捕捉精细物体边界。实验表明，尽管卷积模型如UNet和Attention UNet提供强基线性能，DeepLabV3的成果与SegFormer相当，证明了空洞卷积和多尺度上下文聚合在捕捉复杂手术场景中的有效性。基于架构的模型如SegFormer进一步增强了全局上下文理解，从而在不同器械外观和手术条件下的泛化能力得到提升。本工作为手术AI应用中的分割模型选择提供了全面比较和实用见解，突显了卷积和基于架构方法之间的权衡。

英文摘要：

Accurate segmentation of surgical instruments in robotic-assisted surgery is critical for enabling context-aware computer-assisted interventions, such as tool tracking, workflow analysis, and autonomous decision-making. In this study, we benchmark five deep learning architectures-UNet, UNet, DeepLabV3, Attention UNet, and SegFormer on the SAR-RARP50 dataset for multi-class semantic segmentation of surgical instruments in real-world radical prostatectomy videos. The models are trained with a compound loss function combining Cross Entropy and Dice loss to address class imbalance and capture fine object boundaries. Our experiments reveal that while convolutional models such as UNet and Attention UNet provide strong baseline performance, DeepLabV3 achieves results comparable to SegFormer, demonstrating the effectiveness of atrous convolution and multi-scale context aggregation in capturing complex surgical scenes. Transformer-based architectures like SegFormer further enhance global contextual understanding, leading to improved generalization across varying instrument appearances and surgical conditions. This work provides a comprehensive comparison and practical insights for selecting segmentation models in surgical AI applications, highlighting the trade-offs between convolutional and transformer-based approaches.

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。

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