Automatic choroid layer segmentation in OCT images via context efficient adaptive network

Yan, Qifeng; Gu, Yuanyuan; Zhao, Jinyu; Wu, Wenjun; Ma, Yuhui; Liu, Jiang; Zhang, Jiong; Zhao, Yitian

doi:10.1007/s10489-022-03723-w

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Efficient channel attention requires only a few parameters to produce remarkable results [42]. Due to the superior performance of ECA-Net, many studies have been conducted to use it for the adaptation of channel feature weights [43,44]. However, the ECA-Net input is mainly two-dimensional features, and this study improves its input structure for channel weight adaptation of one-dimensional features.…”

Section: Eca-netmentioning

confidence: 99%

Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion

Yu,

Fu,

Song

et al. 2023

Meas. Sci. Technol.

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Current deep-learning methods are often based on significantly large quantities of labeled fault data for supervised training. In practice, it is difficult to obtain samples of rolling bearing failures. In this paper, a transfer learning-based feature fusion convolutional neural network approach for bearing fault diagnosis is proposed. Specifically, the raw vibration signal features and the corresponding time-frequency image features of the input data are extracted by a one-dimensional convolutional neural network and a pre-trained ConvNeXt, respectively, and connected by a feature fusion strategy. Then, the fine-tuning method based on transfer learning can effectively reduce the reliance on labeled samples in the target domain. A wide convolution kernel is introduced in the time-domain signal feature extraction to increase the receptive field, which is combined with the channel attention mechanism to further optimize the feature quality. Finally, two common bearing datasets are utilized for fault diagnosis experiments. The experimental results show that the proposed model achieves an average accuracy of more than 98.63% in both cross-working conditions and cross-device diagnosis tasks. Meanwhile, anti-noise experiments and ablation experiments further validate the accuracy and robustness of the proposed method.

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Section: Eca-netmentioning

confidence: 99%

Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion

Yu,

Fu,

Song

et al. 2023

Meas. Sci. Technol.

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“…These methods rely on manual parameter settings, and usually yield low efficiency, which limits their segmentation accuracy and makes them difficult to apply in clinical practice. With the emergence and development of deep learning, several Convolutional Neural Network (CNN) models have been applied to choroidal layer segmentation ( Chen et al, 2015 ; Sui et al, 2017 ; He et al, 2021 ; Yan et al, 2022 ). The powerful feature learning capability of CNN has significantly improved choroid segmentation accuracy and efficiency over the last decade.…”

Section: Introductionmentioning

confidence: 99%

Choroidal layer segmentation in OCT images by a boundary enhancement network

Gong²,

Hao³

et al. 2022

Front. Cell Dev. Biol.

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Morphological changes of the choroid have been proved to be associated with the occurrence and pathological mechanism of many ophthalmic diseases. Optical Coherence Tomography (OCT) is a non-invasive technique for imaging of ocular biological tissues, that can reveal the structure of the retinal and choroidal layers in micron-scale resolution. However, unlike the retinal layer, the interface between the choroidal layer and the sclera is ambiguous in OCT, which makes it difficult for ophthalmologists to identify with certainty. In this paper, we propose a novel boundary-enhanced encoder-decoder architecture for choroid segmentation in retinal OCT images, in which a Boundary Enhancement Module (BEM) forms the backbone of each encoder-decoder layer. The BEM consists of three parallel branches: 1) a Feature Extraction Branch (FEB) to obtain feature maps with different receptive fields; 2) a Channel Enhancement Branch (CEB) to extract the boundary information of different channels; and 3) a Boundary Activation Branch (BAB) to enhance the boundary information via a novel activation function. In addition, in order to incorporate expert knowledge into the segmentation network, soft key point maps are generated on the choroidal boundary, and are combined with the predicted images to facilitate precise choroidal boundary segmentation. In order to validate the effectiveness and superiority of the proposed method, both qualitative and quantitative evaluations are employed on three retinal OCT datasets for choroid segmentation. The experimental results demonstrate that the proposed method yields better choroid segmentation performance than other deep learning approaches. Moreover, both 2D and 3D features are extracted for statistical analysis from normal and highly myopic subjects based on the choroid segmentation results, which is helpful in revealing the pathology of high myopia. Code is available at https://github.com/iMED-Lab/Choroid-segmentation.

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Automatic choroid layer segmentation in OCT images via context efficient adaptive network

Cited by 2 publications

References 27 publications

Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion

Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion

Choroidal layer segmentation in OCT images by a boundary enhancement network

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