Depth Anything in Medical Images: A Comparative Study
Published in arXiv Preprint, 2024
Recommended citation: Han, John J., et al. "Depth Anything in Medical Images: A Comparative Study."�arXiv preprint arXiv:2401.16600�(2024). https://arxiv.org/abs/2401.16600
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.
Recommended citation: Han, John J., et al. “Depth Anything in Medical Images: A Comparative Study.”�arXiv preprint arXiv:2401.16600�(2024).