Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional...

Area of Science:

• Medical Imaging
• Computer Vision
• Artificial Intelligence

Background:

• Colorectal cancer is a leading cause of death globally.
• Conventional colonoscopy misses over 20% of premalignant lesions due to poor contrast and imaging limitations.
• Accurate 3D reconstruction of colon topography is crucial for improving lesion detection.

Purpose of the Study:

• To develop and validate a novel deep learning framework for monocular endoscopy depth estimation.
• To reconstruct colon surface topography from single colonoscopy images.
• To enhance computer-aided detection of colorectal lesions.

Main Methods:

• A joint deep convolutional neural network-conditional random field (CNN-CRF) framework was developed for depth estimation.
• Synthetic colonoscopy images were generated using an endoscope camera model and extensive rendering.
• The model was trained on synthetic data and validated using real porcine colonoscopy images with CT ground truth.

Main Results:

• The CNN-CRF approach achieved a relative depth error of 0.152 for synthetic images and 0.242 for real images.
• Demonstrated successful reconstruction of colon mucosal topography from conventional colonoscopy images.
• The method shows potential for integration into existing endoscopy systems.

Conclusions:

• The proposed CNN-CRF method accurately estimates depth and reconstructs colon topography from monocular endoscopy images.
• This technique can significantly improve the detection, segmentation, and classification of colorectal lesions.
• The approach offers a foundation for advanced computer-aided detection in colonoscopy.