Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Latent Priors

Researchers propose a self-supervised framework for monocular depth and pose estimation in endoscopy using a Generative Latent Bank and VAE to improve 3D mapping of the gastrointestinal tract. The method achieves superior performance over existing self-supervised approaches on standard endoscopic datasets without requiring synthetic training data.

This research addresses a fundamental challenge in medical imaging: accurate 3D reconstruction from single-camera endoscopic video. Traditional depth estimation methods struggle with endoscopy's unique constraints—monocular input, poor texture variation, and challenging lighting conditions within the GI tract. The proposed framework tackles these limitations through two key innovations: leveraging latent feature priors from natural image datasets to enhance depth prediction realism, and reformulating pose estimation as a latent variable problem within a VAE framework.

The approach represents an evolution in self-supervised medical imaging, moving away from reliance on synthetic datasets that often fail to generalize to real clinical conditions. By incorporating a Generative Latent Bank conditioned on diverse depth scenes, the model gains implicit knowledge about realistic depth distributions while maintaining the flexibility of self-supervised learning. The VAE-based pose estimation component addresses scale ambiguity and directional sensitivity issues endemic to monocular systems.

For medical device manufacturers and clinical researchers, this development could accelerate adoption of computer-assisted endoscopy systems for lesion characterization and quantitative assessment. Accurate 3D mapping enables more precise measurements of gastrointestinal abnormalities, potentially improving diagnostic consistency and treatment planning. The framework's demonstrated superiority on SimCol and EndoSLAM benchmarks suggests readiness for broader validation on diverse endoscopy platforms.

Future work likely involves clinical validation on real patient datasets and integration with existing endoscopy hardware. The self-supervised approach's ability to work without extensive labeled data positions it favorably for deployment in resource-constrained healthcare settings.

• →Self-supervised framework combines Generative Latent Bank and VAE for robust endoscopic depth and pose estimation
• →Method outperforms published self-supervised baselines on SimCol and EndoSLAM benchmark datasets
• →Approach eliminates dependency on synthetic training data that typically fails to generalize in clinical conditions
• →Reformulated pose estimation as latent variable problem improves scale stability and directional sensitivity
• →Technology enables accurate 3D gastrointestinal tract mapping for quantitative lesion characterization