Synthetic Data in Supervised Monocular Depth Estimation of Laparoscopic Liver Images

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 syn-
thetic data. This leads to the question, how well does the syn-
thetic data allow the prediction of depth information on clini-
cal data. To evaluate this, the synthetic data is used to train and
optimize a U-Net, including hyperparameter tuning and aug-
mentation. 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 syn-
thetic data sets can be used for training, with an accuracy of
over 77% and a RMSE below 10 mm on the synthetic data set,
do well on resembling clinical data, but also have limitations
due to the complexity of clinical environments. ... mehrSynthetic data
sets are a promising approach allowing monocular depth esti-
mation in fields with otherwise lacking data.