Purpose: The principal objective of this study was to develop and evaluate a deep learning model for segmenting the common iliac vein (CIV) from intraoperative endoscopic videos during oblique lateral interbody fusion for L5/S1 (OLIF51), a minimally invasive surgical procedure for degenerative lumbosacral spine diseases. The study aimed to address the challenge of intraoperative differentiation of the CIV from surrounding tissues to minimize the risk of vascular damage during the surgery.

Methods: We employed two convolutional neural network (CNN) architectures: U-Net and U-Net++ with a ResNet18 backbone, for semantic segmentation. Gamma correction was applied during image preprocessing to improve luminance contrast between the CIV and adjacent tissues. We used a dataset of 614 endoscopic images from OLIF51 surgeries for model training, validation, and testing.

Results: The U-Net++/ResNet18 model outperformed, achieving a Dice score of 0.70, indicating superior ability in delineating the position and shape of the CIV compared to the U-Net/ResNet18 model, which achieved a Dice score of 0.59. Gamma correction increased the differentiation between the CIV and the artery, improving the Dice score from 0.44 to 0.70.

Conclusion: The findings demonstrate that deep learning models, especially the U-Net++ with ResNet18 enhanced by gamma correction preprocessing, can effectively segment the CIV in intraoperative videos. This approach has the potential to significantly improve intraoperative assistance and reduce the risk of vascular injury during OLIF51 procedures, despite the need for further research and refinement of the model for clinical application.

Keywords: Deep learning; Gamma correction; Oblique lateral lumbar interbody fusion (OLIF) 51; Preprocessing; Segmentation; Vein segmentation.

© 2025. The Author(s).

目的： 本研究的主要目标是开发和评估一种深度学习模型，用于在L5/S1侧方斜外侧椎间融合术（OLIF51）的手术内窥镜视频中分割出髂总静脉（CIV），这是一种用于治疗退行性腰骶椎疾病的微创手术。该研究旨在解决手术过程中区分CIV和周围组织的难题，以减少手术期间血管损伤的风险。

方法： 我们采用了两种卷积神经网络（CNN）架构：U-Net和带有ResNet18骨干的U-Net++进行语义分割。在图像预处理过程中应用了伽马校正，以提高CIV与相邻组织之间的亮度对比度。我们使用了一组614张来自OLIF51手术的内窥镜图片的数据集来进行模型训练、验证和测试。

结果： U-Net++/ResNet18模型优于U-Net/ResNet18模型，Dice分数为0.70，表明其在界定CIV的位置和形状方面的能力更强。U-Net/ResNet18模型的Dice分数为0.59。伽马校正增加了CIV与动脉之间的区分度，将Dice分数从0.44提高到了0.70。

结论： 研究结果表明，深度学习模型（特别是通过伽马校正预处理增强的带有ResNet18骨干的U-Net++）能够有效地在手术视频中分割CIV。这一方法有望显著提高术中的辅助作用，并减少OLIF51过程中血管损伤的风险，尽管还需要进一步的研究和对该模型进行优化以适用于临床应用。

关键词： 深度学习；伽马校正；侧方斜外侧腰椎椎间融合（OLIF）51；预处理；分割；静脉分割。