SHADeS: self-supervised monocular depth estimation through non-Lambertian image decomposition
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By
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Rema Daher
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Francisco Vasconcelos
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Danail Stoyanov
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May 13, 2025
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Clinical Scorecard: Self-Supervised Monocular Depth Estimation via Non-Lambertian Image Decomposition in SHADeS
At a Glance
| Category | Detail |
|---|
| Condition | Visibility challenges in endoscopic imaging due to specular reflections and illumination variations |
| Key Mechanisms | Joint estimation of depth, albedo, shading, and specular reflections using a non-Lambertian image decomposition model |
| Target Population | Patients undergoing colonoscopy for colorectal cancer screening and diagnosis |
| Care Setting | Endoscopic imaging and surgical navigation in clinical gastroenterology |
Key Highlights
- Proposes a novel self-supervised monocular depth estimation framework robust to specular reflections in endoscopy.
- Model decomposes images into albedo, shading, and specular reflection components, improving artefact reduction.
- Enables implicit specularity segmentation and specularity removal through image inpainting.
Guideline-Based Recommendations
Diagnosis
- Utilize monocular depth estimation to improve 3D reconstruction and visibility during colonoscopy.
- Incorporate models that account for non-Lambertian surfaces to handle specular reflections.
Management
- Apply self-supervised learning approaches trained on real endoscopy data to enhance depth estimation accuracy.
- Use image decomposition techniques to separate specular highlights from underlying tissue features.
Monitoring & Follow-up
- Assess model performance on both real (Hyper Kvasir) and phantom colon datasets (C3VD) to ensure robustness.
- Monitor the quality of specularity segmentation and inpainting outputs to maintain image clarity.
Risks
- Be aware that traditional Lambertian-based models may produce artefacts due to inability to separate specular reflections.
- Consider that sub-optimal focus and motion blur in endoscopy can affect depth estimation accuracy.
Patient & Prescribing Data
Patients undergoing colonoscopy for colorectal cancer screening and diagnosis
Enhanced 3D reconstruction and navigation through improved depth estimation may aid early detection and characterization of colorectal lesions.
Clinical Best Practices
- Incorporate self-supervised monocular depth estimation frameworks that model non-Lambertian reflectance for endoscopic imaging.
- Use joint estimation of depth and light components to improve visualization and reduce artefacts from specular reflections.
- Leverage specularity segmentation masks and inpainting to enhance image quality and assist clinical interpretation.
References
