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<p>Laparoscopic liver resection (LLR) is challenging due to the complex and variable intrahepatic vascular anatomy, limited surgical field of view, and lack of tactile feedback, which collectively increase the risk of intraoperative injury. Accurate identification of anatomical structures is therefore essential for safe LLR. This study explores Vivim, a video segmentation model based on the Mamba architecture—a state-space model that integrates the long-range dependency modeling of transformers with the efficiency of recurrent networks. Evaluated on a multicenter dataset of 15,865 annotated frames from 45 videos, Vivim outperformed several image-based and video-based baselines, including U-Net, DeepLab v3 + , nnU-Net, FPN + EfficientNetV2-L, SegFormer, ConvLSTM-U-Net, ViViT and SegFormer + T-Mamba, in segmenting the Glissonean pedicle (GP) and hepatic vein (HV). By leveraging Mamba’s selective scanning and linear complexity, Vivim achieved Dice scores of 0.71 and 0.66 in single- and multi-target segmentation tasks, respectively, while maintaining real-time inference at 25 fps. The model demonstrated strong generalization on a cross-center external test set and robustness to typical surgical challenges. Clinically validated by 13 surgeons, Vivim improved recognition speed and accuracy, aiding intraoperative decision-making. Despite difficulties in differentiating similar vessels, this Mamba-based framework represents a promising step toward AI-assisted surgical navigation, bridging laboratory precision and clinical reliability.</p>

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Real-time anatomy recognition in laparoscopic liver resection using video segmentation AI model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月10日

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  • Haisu Tao,
  • Kangwei Guo,
  • Yijun Yang,
  • Xincheng Yao,
  • Ruiqiang Xiao,
  • Xiaojun Zeng,
  • Xuanshuang Tang,
  • Lei Zhu,
  • Yinling Qian,
  • Jian Yang

摘要

Laparoscopic liver resection (LLR) is challenging due to the complex and variable intrahepatic vascular anatomy, limited surgical field of view, and lack of tactile feedback, which collectively increase the risk of intraoperative injury. Accurate identification of anatomical structures is therefore essential for safe LLR. This study explores Vivim, a video segmentation model based on the Mamba architecture—a state-space model that integrates the long-range dependency modeling of transformers with the efficiency of recurrent networks. Evaluated on a multicenter dataset of 15,865 annotated frames from 45 videos, Vivim outperformed several image-based and video-based baselines, including U-Net, DeepLab v3 + , nnU-Net, FPN + EfficientNetV2-L, SegFormer, ConvLSTM-U-Net, ViViT and SegFormer + T-Mamba, in segmenting the Glissonean pedicle (GP) and hepatic vein (HV). By leveraging Mamba’s selective scanning and linear complexity, Vivim achieved Dice scores of 0.71 and 0.66 in single- and multi-target segmentation tasks, respectively, while maintaining real-time inference at 25 fps. The model demonstrated strong generalization on a cross-center external test set and robustness to typical surgical challenges. Clinically validated by 13 surgeons, Vivim improved recognition speed and accuracy, aiding intraoperative decision-making. Despite difficulties in differentiating similar vessels, this Mamba-based framework represents a promising step toward AI-assisted surgical navigation, bridging laboratory precision and clinical reliability.