Computer-assisted surgical (CAS) systems enhance surgical execution and outcomes by providing advanced support to surgeons. These systems often rely on deep learning models trained on complex, challenging-to-annotate data. While synthetic data generation can address these challenges, enhancing the realism of such data is crucial. This work introduces a multi-stage pipeline for generating realistic synthetic data, featuring a fully-fledged surgical simulator that automatically produces all necessary annotations for modern CAS systems. This simulator generates a wide set of annotations that surpass those available in public synthetic datasets. Additionally, it offers a more complex and realistic simulation of surgical interactions, including the dynamics between surgical instruments and deformable anatomical environments, outperforming existing approaches. To further bridge the visual gap between synthetic and real data, we propose a lightweight and flexible image-to-image translation method based on Stable Diffusion (SD) and Low-Rank Adaptation (LoRA). This method leverages a limited amount of annotated data, enables efficient training, and maintains the integrity of annotations generated by our simulator. The proposed pipeline is experimentally validated and can translate synthetic images into images with real-world characteristics, which can generalize to real-world context, thereby improving both training and CAS guidance. The code and the dataset are available at https://github.com/SanoScience/SimuScope.

该研究解决了计算机辅助外科系统中合成数据的现实性不足问题，提出了一种多阶段数据生成管道，结合了一款全面的外科模拟器，能够自动生成所需的注释，提升真实感和复杂度。研究表明，该方法有效地缩小了合成数据与真实数据之间的视觉差距，对外科培训和指导具有显著的潜在影响。

SimuScope：通过外科模拟和扩散模型生成真实的内窥镜合成数据集