.png){kind=icon}
返回
Self-supervised monocular depth estimation for gastrointestinal endoscopy
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cmpb.2023.107619.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Background and objective: Gastrointestinal (GI) endoscopy represents a promising tool for GI cancer screening. However, the limited field of view and uneven skills of endoscopists make it remains diffi-cult to accurately identify polyps and follow up on precancerous lesions under endoscopy. Estimating depth from GI endoscopic sequences is essential for a series of AI-assisted surgical techniques. Nonethe-less, depth estimation algorithm of GI endoscopy is a challenging task due to the particularity of the environment and the limitation of datasets. In this paper, we propose a self-supervised monocular depth estimation method for GI endoscopy. Methods: A depth estimation network and a camera ego-motion estimation network are firstly con-structed to obtain the depth information and pose information of the sequence respectively, and then the model is enabled to perform self-supervised training by calculating the multi-scale structural simi-larity with L1 norm (MS-SSIM+L1) loss function between the target frame and the reconstructed image as part of the loss of the training network. The MS-SSIM+L1 loss function is good for reserving high-frequency information and can maintain the invariance of brightness and color. Our model consists of the U-shape convolutional network with the dual-attention mechanism, which is beneficial to capture muti-scale contextual information, and greatly improves the accuracy of depth estimation. We evaluated our method qualitatively and quantitatively with different state-of-the-art methods. Results and conclusions: The experimental results manifest that our method has superior generality, achieving lower error metrics and higher accuracy metrics on both the UCL dataset and the Endoslam dataset. The proposed method has also been validated with clinical GI endoscopy, demonstrating the po-tential clinical value of the model. (c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Gastrointestinal endoscopy
Monocular depth estimation
Dual-attention mechanism
Self-supervised learning
期刊
IF:
4.8
论文数:
7.0K
被引数:
2.1W
机构
引用论文
StaSiS-Net: A stacked and siamese disparity estimation network for depth reconstruction in modern 3D laparoscopy.png)
MEDICAL IMAGE ANALYSIS
MEDICAL IMAGE ANALYSISIF11.8
PREAI
分享收藏Frontiers of Robotic Colonoscopy: A Comprehensive Review of Robotic Colonoscopes and Technologies
OAAI
OAAI分享收藏Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy基于深度学习和条件随机场的深度估计和传统内窥镜的地形重建MEDICAL IMAGE ANALYSISOAAI
MEDICAL IMAGE ANALYSISIF11.8
OAAI分享收藏
查看更多内容