Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation

2026-06-15 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision · Depth: Expert, quick

Summary

A new unified framework addresses the challenges of accurate vision-based navigation in monocular endoscopy, which struggles with limited depth cues, weak tissue texture, and non-rigid deformation. The proposed approach, Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation, combines a synthetic data pipeline for precise geometric supervision with Hierarchy-Aware Geometry-Semantic Adaptation. This adaptation method, a structured alternative to standard LoRA, selectively inserts low-rank adapters across the transformer hierarchy and employs layer-wise training objectives. This encourages geometric correspondence in intermediate features and semantic consistency in deeper features. Experiments on public and proprietary datasets demonstrate improved geometric and semantic representation quality, enhancing downstream tasks like pose estimation and monocular depth estimation. The learned representations exhibit favorable synthetic-to-real transfer on clinical bronchoscopy and offer effective initialization for adaptation to sinus endoscopy and colonoscopy, even with limited supervision, while also scaling well with model size and training data.

Key takeaway

For Computer Vision Engineers developing vision-based navigation systems for monocular endoscopy, you should consider integrating geometry-guided adaptation techniques. This approach, specifically Hierarchy-Aware Geometry-Semantic Adaptation, offers a robust method to achieve geometry-consistent and domain-robust image representations. It can significantly improve performance on tasks like pose estimation and depth prediction, providing a strong initialization for adapting to diverse clinical scenarios such as bronchoscopy, sinus endoscopy, and colonoscopy, even with limited supervision.

Key insights

Hierarchy-Aware Geometry-Semantic Adaptation improves monocular endoscopy navigation by integrating geometric supervision and structured foundation model adaptation for robust representations.

Principles

• Geometric supervision enhances endoscopic representation learning.
• Structured adaptation improves feature consistency across hierarchies.
• Layer-wise objectives guide geometric and semantic feature learning.

Method

The framework combines a synthetic data pipeline for geometric supervision with Hierarchy-Aware Geometry-Semantic Adaptation. This structured LoRA alternative inserts low-rank adapters selectively across the transformer hierarchy, coupling them with layer-wise training objectives for geometric and semantic consistency.

In practice

• Use synthetic data for geometric supervision in endoscopy.
• Apply Hierarchy-Aware Adaptation for robust feature learning.
• Adapt pre-trained models for bronchoscopy, sinus, colonoscopy.

Topics

• Monocular Endoscopy
• Image-Guided Navigation
• Foundation Model Adaptation
• Geometric Supervision
• LoRA
• Pose Estimation

Best for: AI Scientist, Computer Vision Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.