Depth Anything in Medical Images: A Comparative Study
John J. Han Ayberk Acar Callahan Henry Jie Ying Wu
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.