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Gastrointestinal image stitching based on improved unsupervised algorithm
Rui Yan1, Yu Jiang1, Chenhao Zhang1
1College of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Plos One
|September 18, 2024
Summary
This study introduces an improved unsupervised deep learning framework for stitching gastrointestinal images, enhancing the field of view and reducing missed detections during endoscopy. The method improves image quality metrics and addresses limitations of supervised learning approaches.
Area of Science:
- Computer Vision
- Medical Imaging
- Gastroenterology
Background:
- Image stitching is crucial for creating seamless, high-resolution images from multiple overlapping views.
- Traditional methods face challenges with ghosts, seams, and limited field of view, particularly in medical applications like gastroenteroscopy.
- Improving gastroenteroscopy by expanding the field of view and reducing missed detections is essential for accurate diagnosis.
Purpose of the Study:
- To enhance the field of view in gastroenteroscopy and decrease missed detection rates.
- To propose an improved unsupervised deep learning framework for panoramic image stitching of the gastrointestinal tract.
- To establish a benchmark dataset and training framework for unsupervised deep gastrointestinal image splicing.
Main Methods:
- An improved depth framework for unsupervised panoramic image stitching was developed.
- Preprocessing for aberration correction of monocular endoscope images was implemented.
- A C2f module was integrated into the image reconstruction network to enhance feature extraction capabilities.
- A new dataset, GASE-Dataset, was created for training and evaluation.
Main Results:
- Significant improvements were observed in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and RMSE_SW.
- The image splicing time was maintained within an acceptable range.
- The proposed method demonstrated enhanced performance compared to traditional image stitching techniques.
- The approach addresses limitations of supervised learning, including data scarcity and generalization issues.
Conclusions:
- The developed unsupervised deep learning framework effectively improves gastrointestinal image stitching.
- This method enhances the field of view and reduces missed detections, offering valuable assistance in gastrointestinal examinations.
- The proposed techniques overcome key challenges in current image stitching schemes, paving the way for more comprehensive endoscopic evaluations.
