ArticleBiomedical optics express2018
Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Fusing multispectral information for retinal layer segmentation.NPJ digital medicine · 2025Article
- Wavelet attention network for the segmentation of layer structures on OCT images.Biomedical optics express · 2022Article
- Connectivity-based deep learning approach for segmentation of the epithelium inBiomedical optics express · 2021Article
- Tissue self-attention network for the segmentation of optical coherence tomography images on the esophagus.Biomedical optics express · 2021Article
- Adversarial convolutional network for esophageal tissue segmentation on OCT images.Biomedical optics express · 2020Article
- Robust, accurate depth-resolved attenuation characterization in optical coherence tomography.Biomedical optics express · 2020Article
- Parallel deep neural networks for endoscopic OCT image segmentation.Biomedical optics express · 2019Article
- Automatic three-dimensional segmentation of endoscopic airway OCT images.Biomedical optics express · 2019Article
- Fast esophageal layer segmentation in OCT images of guinea pigs based on sparse Bayesian classification and graph search.Biomedical optics express · 2019Article
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Authors and funding
8 authors.
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Abstract
Automatic segmentation of esophageal layers in OCT images is crucial for studying esophageal diseases and computer-assisted diagnosis. This work aims to improve the current techniques to increase the accuracy and robustness for esophageal OCT image segmentation. A two-step edge-enhanced graph search (EEGS) framework is proposed in this study. Firstly, a preprocessing scheme is applied to suppress speckle noise and remove the disturbance in the esophageal structure. Secondly, the image is formulated into a graph and layer boundaries are located by graph search. In this process, we propose an edge-enhanced weight matrix for the graph by combining the vertical gradients with a Canny edge map. Experiments on esophageal OCT images from guinea pigs demonstrate that the EEGS framework is more robust and more accurate than the current segmentation method. It can be potentially useful for the early detection of esophageal diseases.
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