Depth Anything in Medical Images: A Comparative Study
This work addresses the challenge of depth estimation in medical imaging where ground truth data is unavailable, but it is incremental as it compares existing models without introducing new methods.
The study evaluated the zero-shot performance of the Depth Anything Model on medical endoscopic and laparoscopic scenes for monocular depth estimation, finding that while impressive, it did not outperform other models in speed or accuracy.
Monocular depth estimation (MDE) is a critical component of many medical tracking and mapping algorithms, particularly from endoscopic or laparoscopic video. However, because ground truth depth maps cannot be acquired from real patient data, supervised learning is not a viable approach to predict depth maps for medical scenes. Although self-supervised learning for MDE has recently gained attention, the outputs are difficult to evaluate reliably and each MDE's generalizability to other patients and anatomies is limited. This work evaluates the zero-shot performance of the newly released Depth Anything Model on medical endoscopic and laparoscopic scenes. We compare the accuracy and inference speeds of Depth Anything with other MDE models trained on general scenes as well as in-domain models trained on endoscopic data. Our findings show that although the zero-shot capability of Depth Anything is quite impressive, it is not necessarily better than other models in both speed and performance. We hope that this study can spark further research in employing foundation models for MDE in medical scenes.