ArticleBiomedical optics express2020
Adversarial convolutional network for esophageal tissue segmentation on OCT images.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Can artificial intelligence improve the diagnosis and management of patients with eosinophilic esophagitis?World journal of gastroenterology · 2025Review
- Expert Recommendations on the Diagnosis of Eosinophilic Esophagitis in the United Arab Emirates.Cureus · 2024Review
- Wavelet attention network for the segmentation of layer structures on OCT images.Biomedical optics express · 2022Article
- Stacked dilated convolutions and asymmetric architecture for U-Net-based medical image segmentation.Computers in biology and medicine · 2022Article
- Epidural anesthesia needle guidance by forward-view endoscopic optical coherence tomography and deep learning.Scientific reports · 2022Article
- Esophageal optical coherence tomography image synthesis using an adversarially learned variational autoencoder.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
- Deep-learning-aided forward optical coherence tomography endoscope for percutaneous nephrostomy guidance.Biomedical optics express · 2021Article
- IA-net: informative attention convolutional neural network for choroidal neovascularization segmentation in OCT images.Biomedical optics express · 2020Article
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Authors and funding
4 authors.
Funding
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Abstract
Automatic segmentation is important for esophageal OCT image processing, which is able to provide tissue characteristics such as shape and thickness for disease diagnosis. Existing automatical segmentation methods based on deep convolutional networks may not generate accurate segmentation results due to limited training set and various layer shapes. This study proposed a novel adversarial convolutional network (ACN) to segment esophageal OCT images using a convolutional network trained by adversarial learning. The proposed framework includes a generator and a discriminator, both with U-Net alike fully convolutional architecture. The discriminator is a hybrid network that discriminates whether the generated results are real and implements pixel classification at the same time. Leveraging on the adversarial training, the discriminator becomes more powerful. In addition, the adversarial loss is able to encode high order relationships of pixels, thus eliminating the requirements of post-processing. Experiments on segmenting esophageal OCT images from guinea pigs confirmed that the ACN outperforms several deep learning frameworks in pixel classification accuracy and improves the segmentation result. The potential clinical application of ACN for detecting eosinophilic esophagitis (EoE), an esophageal disease, is also presented in the experiment.
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Registered trials
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