Synthetic Data in Supervised Monocular Depth Estimation of Laparoscopic Liver Images

Abstract Monocular depth estimation is an important topic in minimally invasive surgery, providing valuable information for downstream application, like navigation systems. Deep learning for this task requires high amount of training data for an accurate and robust model. Especially in the medical field acquiring ground truth depth information is rarely possible due to patient security and technical limitations. This problem is being tackled by many approaches including the use of synthetic data. This leads to the question, how well does the synthetic data allow the prediction of depth information on clinical data. To evaluate this, the synthetic data is used to train and optimize a U-Net, including hyperparameter tuning and augmentation. The trained model is then used to predict the depth on clinical image and analyzed in quality, consistency over the same scene, time and color. The results demonstrate that synthetic data sets can be used for training, with an accuracy of over 77% and a RMSE below 10mm on the synthetic data set, do well on resembling clinical data, but also have limitations due to the complexity of clinical environments. Synthetic data sets are a promising approach allowing monocular depth estimation in fields with otherwise lacking data.