Tseng Li-An, Tsai Yuan-Chih, Bai Meng-Yi, Li Mei-Fang, Lee Yi-Liang, Chiang Kai-Jo, Wang Yu-Chi, Guo Jing-Ming
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Diagnostics (Basel). 2025 Jul 30;15(15):1917. doi: 10.3390/diagnostics15151917.
: Automated surgical navigation can be separated into three stages: (1) organ identification and localization, (2) identification of the organs requiring further surgery, and (3) automated planning of the operation path and steps. With its ideal visual and operating system, the da Vinci surgical system provides a promising platform for automated surgical navigation. This study focuses on the first step in automated surgical navigation by identifying organs in gynecological surgery. : Due to the difficulty of collecting da Vinci gynecological endoscopy data, we propose DeepVinci, a novel end-to-end high-performance encoder-decoder network based on convolutional neural networks (CNNs) for pixel-level organ semantic segmentation. Specifically, to overcome the drawback of a limited field of view, we incorporate a densely multi-scale pyramid module and feature fusion module, which can also enhance the global context information. In addition, the system integrates an edge supervision network to refine the segmented results on the decoding side. : Experimental results show that DeepVinci can achieve state-of-the-art accuracy, obtaining dice similarity coefficient and mean pixel accuracy values of 0.684 and 0.700, respectively. : The proposed DeepVinci network presents a practical and competitive semantic segmentation solution for da Vinci gynecological surgery.
（1）器官识别与定位；（2）识别需要进一步手术的器官；（3）手术路径和步骤的自动规划。凭借其理想的视觉和操作系统，达芬奇手术系统为自动手术导航提供了一个有前景的平台。本研究通过识别妇科手术中的器官，聚焦于自动手术导航的第一步。：由于收集达芬奇妇科内窥镜数据存在困难，我们提出了DeepVinci，这是一种基于卷积神经网络（CNN）的新型端到端高性能编码器 - 解码器网络，用于像素级器官语义分割。具体而言，为克服视野有限的缺点，我们纳入了密集多尺度金字塔模块和特征融合模块，这也可以增强全局上下文信息。此外，该系统集成了边缘监督网络，以在解码端细化分割结果。：实验结果表明，DeepVinci可以达到当前最优的精度，分别获得0.684的骰子相似系数和0.700的平均像素精度值。：所提出的DeepVinci网络为达芬奇妇科手术提供了一种实用且有竞争力的语义分割解决方案。
