Summary of Generalizing Monocular Colonoscopy Image Depth Estimation by Uncertainty-based Global and Local Fusion Network, By Sijia Du et al.
Generalizing monocular colonoscopy image depth estimation by uncertainty-based global and local fusion network
by Sijia Du, Chengfeng Zhou, Suncheng Xiang, Jianwei Xu, Dahong Qian
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a robust framework for estimating depth maps from real colonoscopy images. The authors address the challenge of obtaining ground-truth depth maps in clinical scenarios by developing a convolutional neural network (CNN) that combines local feature capture with global information using a Transformer. An uncertainty-based fusion block is designed to enhance generalization and identify complementary contributions from the CNN and Transformer branches. The network can be trained on simulated datasets and generalize directly to unseen clinical data without fine-tuning. The proposed method demonstrates excellent generalization ability across various datasets and anatomical structures, with validation on multiple datasets. Qualitative analysis in real clinical scenarios confirms the robustness of the approach. The integration of local and global features through the CNN-Transformer architecture, along with the uncertainty-based fusion block, improves depth estimation performance and generalization. The significance of this study lies in its potential to serve as a foundation for endoscopic automatic navigation and other clinical tasks, such as polyp detection and segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to estimate how deep things are from colonoscopy images. Colonoscopies are important medical tests that help doctors look inside people’s bodies. But it’s hard to get accurate measurements of depth in these images because the surfaces can be weirdly shaped or reflect light in strange ways. The researchers developed a special kind of computer program called a convolutional neural network (CNN) and a Transformer to help solve this problem. They also added something called an uncertainty-based fusion block to make it work better. The new method is really good at guessing the depth correctly, even when it’s tested on different images or parts of the body. The scientists also showed that their approach works well in real clinical situations. This could be useful for things like helping doctors navigate inside a patient’s body during an endoscopy or detecting and removing polyps. |
Keywords
» Artificial intelligence » Cnn » Depth estimation » Fine tuning » Generalization » Neural network » Transformer
