bibtext: @InProceedings{ Zen_Realistic_MICCAI2024,
                   author = { Zeng, Tianle and Loza Galindo, Gerardo and Hu, Junlei and Valdastri, Pietro and Jones, Dominic },
                   title = { { Realistic Surgical Image Dataset Generation Based On 3D Gaussian Splatting } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: Computer vision technologies markedly enhance the automation capabilities of robotic-assisted minimally invasive surgery (RAMIS) through advanced tool tracking, detection, and localization. However, the limited availability of comprehensive surgical datasets for training represents a significant challenge in this field. This research introduces a novel method that employs 3D Gaussian Splatting to generate synthetic surgical datasets. We propose a method for extracting and combining 3D Gaussian representations of surgical instruments and background operating environments, transforming and combining them to generate high-fidelity synthetic surgical scenarios. We developed a data recording system capable of acquiring images alongside tool and camera poses in a surgical scene. Using this pose data, we synthetically replicate the scene, thereby enabling direct comparisons of the synthetic image quality (27.796±1.796 PSNR). As a further validation, we compared two YOLOv5 models trained on the synthetic and real data, respectively, and assessed their performance in an unseen real-world test dataset. Comparing the performances, we observe an improvement in neural network performance, with the synthetic-trained model outperforming the real-world trained model by 12%, testing both on real-world data.
title: Realistic Surgical Image Dataset Generation Based On 3D Gaussian Splatting
authors: Zeng, Tianle Loza Galindo, Gerardo Hu, Junlei Valdastri, Pietro Jones, Dominic