Conditional GANs for Implicit Domain Adaptation in Endoscopic Depth Estimation

Overview

This study presents a novel approach using conditional generative adversarial networks (cGANs) to estimate depth during colonoscopy, addressing challenges in 3D reconstruction of the colon. The method leverages synthetic data and domain adaptation to improve depth prediction, aiming to enhance polyp detection and procedural quality assessment.

Background

Colorectal adenomas affect nearly 20% of the population and can progress to cancer over several years. Colonoscopy screening is crucial but highly operator-dependent, with non-expert miss rates up to 90%. Current quality metrics like adenoma detection rate and withdrawal time have limitations. Advances in 3D reconstruction during endoscopy could provide real-time guidance to ensure full colon visualization and improve training. However, challenges include reflective, texture-poor tissue and lack of ground truth depth data due to equipment constraints.

Data Highlights

The approach utilizes synthetic RGB images and corresponding depth maps generated from CT-based colon surface meshes rendered in a Unity environment. Conditional GANs are trained on these paired datasets to learn depth estimation. The method incorporates adversarial loss and L1 error to optimize depth map generation, enabling implicit domain adaptation from synthetic to real endoscopic images.

Key Findings

• Conditional GANs can effectively learn depth estimation from synthetic colonoscopy images paired with ground truth depth maps.
• Implicit domain adaptation via cGANs addresses the domain gap between synthetic training data and real endoscopic images.
• The method overcomes challenges of limited texture and reflective surfaces in endoluminal tissue for 3D reconstruction.
• Depth estimation can support real-time guidance to ensure complete colon wall visualization during procedures.
• Improved depth maps may enhance polyp detection algorithms and provide quantitative quality metrics beyond current standards.

Clinical Implications

Integrating cGAN-based depth estimation into colonoscopy could standardize procedural quality by confirming full mucosal inspection. This technology may reduce operator-dependent variability and improve early detection of precancerous lesions. Additionally, it offers a valuable training tool for endoscopists to assess the extent of colon examination objectively.

Conclusion

Conditional generative adversarial networks provide a promising framework for implicit domain adaptation in depth estimation during colonoscopy, potentially advancing 3D reconstruction and procedural quality. This approach lays groundwork for real-time guidance systems and improved colorectal cancer screening outcomes.

References

1. Goodfellow et al. 2014 -- Generative Adversarial Nets
2. Isola et al. 2017 -- Image-to-Image Translation with Conditional Adversarial Nets
3. Bernal et al. 2017 -- Polyp Detection Dataset
4. Bian et al. 2020 -- Depth Prediction for Polyp Detection