RVSeg-Net: An Efficient Feature Pyramid Cascade Network for Retinal Vessel Segmentation

Wang, Wei; Zhong, Jiafu; Wu, Huisi; Wen, Zhenkun; Qin, Jing

doi:10.1007/978-3-030-59722-1_77

Cited by 33 publications

(9 citation statements)

References 27 publications

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“…In recent studies on retinal blood vessel segmentation, due to the limitation of computing capacity, it is difficult for deep learning models to directly segment vessels in retinal images with high resolution. To address this issue, recent methods scaled images during the training stage and restored images to the original resolutions to compute performance metrics during the testing stage [4,5,19]. In this paper, we also adopt this strategy, which all the images in the four datasets are reshaped as 512 × 512, 512 × 512, 512 × 512, and 800 × 800 resolution in the training stage respectively.…”

Section: A Datasetsmentioning

confidence: 99%

“…In this paper, we also adopt this strategy, which all the images in the four datasets are reshaped as 512 × 512, 512 × 512, 512 × 512, and 800 × 800 resolution in the training stage respectively. Similar to most recent methods, after obtaining preliminary segmentation results, we upsampled the obtained segmentation results to the same size as the original image in order to accurately calculate relevant performance indicators [4,5,19].…”

Section: A Datasetsmentioning

confidence: 99%

“…For example, Wang et al combined two identical multi-scale backbone networks with skip connections, effectively integrating the shallow and deep features of the network [18]. Wang et al also proposed a feature pyramid cascade module to capture the scale changes of vessels and alleviate the problem of discontinuous segmentation results by aggregating local and global context information [19]. Wang et al further investigated a hard attention network, which consists of an encoder and three decoders to solve the segmentation of vessel backbone, easy region, and difficult region respectively [20].…”

Section: Introductionmentioning

confidence: 99%

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Global Transformer and Dual Local Attention Network via Deep-Shallow Hierarchical Feature Fusion for Retinal Vessel Segmentation

Zhang

Liu

et al. 2023

IEEE Trans. Cybern.

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Clinically, retinal vessel segmentation is a significant step in the diagnosis of fundus diseases. However, recent methods generally neglect the difference of semantic information between deep and shallow features, which fail to capture the global and local characterizations in fundus images simultaneously, resulting in the limited segmentation performance for fine vessels. In this paper, a global transformer and dual local attention network via deep-shallow hierarchical feature fusion (GT-DLA-dsHFF) are investigated to solve the above limitations. First, the global transformer (GT) is developed to integrate the global information in the retinal image, which effectively captures the long-distance dependence between pixels, alleviating the discontinuity of blood vessels in the segmentation results. Second, the dual local attention (DLA), which is constructed using dilated convolutions with varied dilation rates, unsupervised edge detection, and squeeze-excitation block, is proposed to extract local vessel information, consolidating the edge details in the segmentation result. Finally, a novel deepshallow hierarchical feature fusion (dsHFF) algorithm is studied to fuse the features in different scales in the deep learning framework respectively, which can mitigate the attenuation of valid information in the process of feature fusion. We verified the GT-DLA-dsHFF on four typical fundus image datasets. The experimental results demonstrate our GT-DLA-dsHFF achieves superior performance against the current methods and detailed discussions verify the efficacy of the proposed three modules. Segmentation results on diseased images show the robustness of our proposed GT-DLA-dsHFF. Our codes will be available on https://github.com/YangLibuaa/GT-DLA-dsHFF.

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Section: A Datasetsmentioning

confidence: 99%

Section: A Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global Transformer and Dual Local Attention Network via Deep-Shallow Hierarchical Feature Fusion for Retinal Vessel Segmentation

Zhang

Liu

et al. 2023

IEEE Trans. Cybern.

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“…Conventionally, hand-crafted filters [12,13,16,27] like Gabor [13] and Gaussian-based ones [12] are explored to extract features for pixel selection, vessel clustering and segmentation. Recently, data-driven based methods utilize UNet-based model [17] or its variants [24,25,28,29,10] to achieve significant performance compared with traditional methods. Those deep learning methods focus on the design of UNet structures with better feature representation [28,24], or the decouple of structure and textures of retinal images [29].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, data-driven based methods utilize UNet-based model [17] or its variants [24,25,28,29,10] to achieve significant performance compared with traditional methods. Those deep learning methods focus on the design of UNet structures with better feature representation [28,24], or the decouple of structure and textures of retinal images [29]. However, datadriven methods highly suffer from over-fitting issues when the given training data is insufficient.…”

Section: Introductionmentioning

confidence: 99%

Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels

Zhou¹,

Yu²

2021

Preprint

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Retinal vessel segmentation from retinal images is an essential task for developing the computer-aided diagnosis system for retinal diseases. Efforts have been made on high-performance deep learningbased approaches to segment the retinal images in an end-to-end manner. However, the acquisition of retinal vessel images and segmentation labels requires onerous work from professional clinicians, which results in smaller training dataset with incomplete labels. As known, data-driven methods suffer from data insufficiency, and the models will easily overfit the small-scale training data. Such a situation becomes more severe when the training vessel labels are incomplete or incorrect. In this paper, we propose a Study Group Learning (SGL) scheme to improve the robustness of the model trained on noisy labels. Besides, a learned enhancement map provides better visualization than conventional methods as an auxiliary tool for clinicians. Experiments demonstrate that the proposed method further improves the vessel segmentation performance in DRIVE and CHASE DB1 datasets, especially when the training labels are noisy. Our code is available at https://github.com/SHI-Labs/ SGL-Retinal-Vessel-Segmentation.

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Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels

Zhou

2021

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

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RVSeg-Net: An Efficient Feature Pyramid Cascade Network for Retinal Vessel Segmentation

Cited by 33 publications

References 27 publications

Global Transformer and Dual Local Attention Network via Deep-Shallow Hierarchical Feature Fusion for Retinal Vessel Segmentation

Global Transformer and Dual Local Attention Network via Deep-Shallow Hierarchical Feature Fusion for Retinal Vessel Segmentation

Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels

Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels

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