Fast esophageal layer segmentation in OCT images of guinea pigs based on sparse Bayesian classification and graph search

Gan, Meng; Yang, Na; Yang, Ting; Zhang, Miao; Nao, Sihan; Zhu, Jing; Ge, Hongyu; Wang, Lirong

doi:10.1364/boe.10.000978

Cited by 11 publications

(11 citation statements)

References 46 publications

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“…Among these methods, the median filter is not the best, but it has the advantages of easy parameter setting and fast running speed. The effectiveness of median filter has been proven by numerous studies 37–39 . In this paper, image denoising is achieved with a simple median filter of size 7 × 7.…”

Section: Methodsmentioning

confidence: 99%

Automatic segmentation of hyperreflective dots via focal priors and visual saliency

Zhang

Zhao

et al. 2022

Medical Physics

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Purpose Hyperreflective dots (HRDs) can be observed in spectral domain optical coherence tomography (SD‐OCT), which can provide a sensitive marker in the treatment decision process. Quantitative analyses of HRDs are the key to make appropriate decisions on observation, treatment, and retreatment. The purpose of this study is to automatically and accurately segment HRDs in SD‐OCT B‐scans with diabetic retinopathy (DR). Methods The authors propose an automatic segmentation algorithm of HRDs via focal priors and visual saliency. The algorithm is divided into three stages: segmentation of retinal layers, calculation of the multiscale local contrast saliency map, and adaptive threshold segmentation. First, a method based on improved graph search is used to segment retinal layers to obtain the region of interest (ROI) and the reflectivity estimation of the retinal pigment epithelium (RPE) layer; then, the multiscale local contrast saliency map is obtained by using a local contrast measure, which measures the dissimilarity between the current pixels and corresponding neighborhoods; finally, an adaptive threshold is applied to segment HRDs. Results Experimental results on 20 SD‐OCT B‐scans demonstrate that our method is effective for HRDs segmentation. The average dice similarity coefficient (DSC) and detection accuracy are 71.12% and 85.07%, respectively. Conclusions The proposed method can accurately segment HRDs in SD‐OCT B‐scans with DR and outperforms current state‐of‐the‐art methods. Our method can provide reliable HRDs segmentation to assist ophthalmologists in clinical diagnosis, treatment, disease monitoring, and progression.

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Section: Methodsmentioning

confidence: 99%

Automatic segmentation of hyperreflective dots via focal priors and visual saliency

Zhang

Zhao

et al. 2022

Medical Physics

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“…A more comprehensive evaluation is implemented on the testing dataset consisting of 400 B-scans (200 healthy and 200 EoE) as described in Table 2. In addition to the four deep learning based methods, the table also lists the segmentation result of two graph theory based methods, namely the GTDP [10] and the SBGS [12]. Moreover, manual segmentation result is also presented in the table, where Grader #2 indicates the manual segmentation result of another grader and Grader #1' indicates a second annotation of the same dataset from Grader #1.…”

Section: Comparisons With State-of-the-artmentioning

confidence: 99%

“…The graph-based method requires a priori knowledge like tissue width, which limits its application in some irregular cases. To solve this problem our group designed an automatical segmentation system based on wavelet features and sparse Bayesian classifier in 2019, which is more robust than the traditional gradient-based strategy [12]. Almost at the same time, Li et al proposed a U-Net based framework for an end-to-end esophageal layer segmentation, which introduces deep learning algorithms to the community of esophageal OCT image processing [8].…”

Section: Introductionmentioning

confidence: 99%

Adversarial convolutional network for esophageal tissue segmentation on OCT images

Gan

Zhang

2020

Biomed. Opt. Express

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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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“…1(a). Those irrelevant parts were proved to have negative effects on segmentation performance to some extent [16], [17]. Restricting the learning process within a certain area that only contains layers we concerned is beneficial for determining which layer the pixel belongs to.…”

Section: ) Ucn-i For Esophageal Target Tissue Area Identificationmentioning

confidence: 99%

“…The graph-based methods rely on prior boundary region information, and are sensitive to image quality and intensity variance, which may lead to failure in some cases. In 2019, our group proposed a more robust intelligent segmentation system based on the sparse Bayesian classifier using wavelet coefficients as features [17]. However, the feature extraction process for traditional machine learning requires considerable domain knowledge.…”

Section: Introductionmentioning

confidence: 99%

Dual-Stage U-Shape Convolutional Network for Esophageal Tissue Segmentation in OCT Images

Gan

Wang

2020

IEEE Access

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Automatic segmentation is the crucial step for esophageal optical coherence tomography (OCT) image processing, which is able to highlight diagnosis-related tissue layers and provide characteristics such as shape and thickness for esophageal disease diagnosis. This study proposes a dual-stage framework using a specifically designed encoder-decoder network configuration for accurate and reliable esophageal layer segmentation, which is named as the dual-stage U-shape convolutional network (D-UCN). The proposed approach utilized one UCN to locate the target tissue region, which is followed by another UCN with similar architecture to achieve the final segmentation. In this way, the proposed strategy effectively solves the problems encountered in our previous studies, such as disturbance from neighboring diagnostically unrelated tissues, probe protection sheaths from the imaging equipment and the inevitable speckle noise. Experimental results on esophageal OCT B-scans from C57BL mice demonstrated that the proposed dual-stage framework achieved performance comparable to manual segmentation. The effectiveness and advantages of the dual-stage strategy are also confirmed in comparison with graph theory dynamic program (GTDP) and U-Net. INDEX TERMS Esophageal layer segmentation, Optical coherence tomography, Fully convolutional network, Medical image analysis. FIGURE 1. Demonstration of (a) a typical esophageal OCT image for mouse and (b) the corresponding manual segmentation result. B. FRAMEWORK AND ARCHITECTURE This study proposes a D-UCN framework to segment esophageal layers from OCT images in a dual-stage strategy. It consists of two cascaded parts with the same U-shape network architecture, UCN-I and UCN-II, as shown in Fig.

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Fast esophageal layer segmentation in OCT images of guinea pigs based on sparse Bayesian classification and graph search

Cited by 11 publications

References 46 publications

Automatic segmentation of hyperreflective dots via focal priors and visual saliency

Automatic segmentation of hyperreflective dots via focal priors and visual saliency

Adversarial convolutional network for esophageal tissue segmentation on OCT images

Dual-Stage U-Shape Convolutional Network for Esophageal Tissue Segmentation in OCT Images

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