School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China.
Institute of Medical Robotics and Intelligent Systems, Tianjin University, No. 2 Huake Fifth Road, Binhai Hi-Tech Industrial Development Area, Tianjin, 300392, China.
Int J Comput Assist Radiol Surg. 2023 Aug;18(8):1417-1427. doi: 10.1007/s11548-023-02835-z. Epub 2023 Jan 23.
In robot-assisted minimally invasive surgery (RMIS), smoke produced by laser ablation and cauterization causes degradation in the visual quality of the operating field, increasing the difficulty and risk of surgery. Therefore, it is important and meaningful to remove fog or smoke from the endoscopic video to maintain a clear visual field.
In this paper, we propose a novel method for surgical smoke removal based on the Swin transformer. Our method firstly uses convolutional neural network to extract shallow features, then uses the Swin transformer block to further extract deep features and finally generates smoke-free images.
We conduct quantitative and qualitative experiments on the proposed method, and we also validate the desmoking results in the surgical instrument segmentation task. Extensive experiments on synthetic and real dataset show that the proposed approach has good performance and outperforms the state-of-the-art surgical smoke removal methods.
Our method effectively removes surgical smoke, improves image quality and reduces the risk of RMIS. It provides a clearer visual field for the surgeon, as well as for subsequent visual tasks, such as instrument segmentation, 3D scene reconstruction and surgery automation.
在机器人辅助微创手术（RMIS）中，激光消融和烧灼产生的烟雾会降低手术视野的视觉质量，增加手术的难度和风险。因此，从内窥镜视频中去除雾或烟以保持清晰的视野非常重要且有意义。
在本文中，我们提出了一种基于 Swin 转换器的新型手术烟雾去除方法。我们的方法首先使用卷积神经网络提取浅层特征，然后使用 Swin 转换器块进一步提取深层特征，最后生成无烟雾图像。
我们对所提出的方法进行了定量和定性实验，并且还验证了手术器械分割任务中的去烟效果。在合成和真实数据集上的广泛实验表明，所提出的方法具有良好的性能，优于最新的手术烟雾去除方法。
我们的方法有效地去除了手术烟雾，提高了图像质量，降低了 RMIS 的风险。它为外科医生提供了更清晰的视野，也为后续的视觉任务，如器械分割、3D 场景重建和手术自动化提供了支持。
