Crosslink-Net: Double-Branch Encoder Network via Fusing Vertical and Horizontal Convolutions for Medical Image Segmentation

Yu, Qian; Qi, Lei; Wang, Wuzhang; Shi, Yinghuan

doi:10.1109/tip.2022.3203223

Cited by 15 publications

(3 citation statements)

References 53 publications

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“…Oktay et al [11] introduced a self-attentive structure in U-Net to improve the sensitivity and segmentation accuracy of the model while maintaining the computational efficiency of U-Net. Based on the advantage of spatial attention, Yu et al [34] proposed an attention loss function so as to focus on the segmentation of small-sized targets. Zhang et al [29] added a series of attention gate units to the jump connections of U-Net so as to highlight feature information.…”

Section: Medical Image Segmentationmentioning

confidence: 99%

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A graph‐based edge attention gate medical image segmentation method

Hao

2023

IET Image Processing

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For the purpose of solving the problems of missing edges and low segmentation accuracy in medical image segmentation, a medical image segmentation network (EAGC_UNet++) based on residual graph convolution UNet++ with edge attention gate (EAG) is proposed in the study. With UNet++ as the backbone network, the idea of graph theory is introduced into the model. First, the dropout residual graph convolution block (DropRes_GCN Block) and the traditional convolution structure in UNet++ are used as encoders. Second, EAGs are adopted so that the model pays more attention to image edge features during decoding. Finally, aiming at the imbalance problem of positive and negative samples in medical image segmentation, a new weighted loss function is introduced to enhance segmentation accuracy. In the experimental part, three datasets (LiTS2017, ISIC2018, COVID‐19 CT scans) were used to evaluate the performances of various models; multiple groups of ablation experiments were designed to verify the effectiveness of each part of the model. The experimental results showed that EAGC_UNet++ had better segmentation performance than the other models under three quantitative evaluation indicators and better solved the problem of missing edges in medical image segmentation.

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Section: Medical Image Segmentationmentioning

confidence: 99%

“…Based on the advantage of spatial attention, Yu et al. [34] proposed an attention loss function so as to focus on the segmentation of small‐sized targets. Zhang et al.…”

Section: Related Studiesmentioning

confidence: 99%

A graph‐based edge attention gate medical image segmentation method

Hao

2023

IET Image Processing

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“…proposed a three-layer attention-based segmentation network, combining a three-layer attention mechanism with parallel multi-scale feature optimization to achieve precise segmentation of COVID lesions. (Yu et al, 2022). improved the network's ability to perceive features in infection regions at different scales by combining a dual-branch encoder structure with spatial attention.…”

Section: Open Access Edited Bymentioning

confidence: 99%

ERGPNet: lesion segmentation network for COVID-19 chest X-ray images based on embedded residual convolution and global perception

Yue,

Yang,

Zhao

et al. 2023

Front. Physiol.

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The Segmentation of infected areas from COVID-19 chest X-ray (CXR) images is of great significance for the diagnosis and treatment of patients. However, accurately and effectively segmenting infected areas of CXR images is still challenging due to the inherent ambiguity of CXR images and the cross-scale variations in infected regions. To address these issues, this article proposes a ERGPNet based on embedded residuals and global perception, to segment lesion regions in COVID-19 CXR images. First, aiming at the inherent fuzziness of CXR images, an embedded residual convolution structure is proposed to enhance the ability of internal feature extraction. Second, a global information perception module is constructed to guide the network in generating long-distance information flow, alleviating the interferences of cross-scale variations on the algorithm’s discrimination ability. Finally, the network’s sensitivity to target regions is improved, and the interference of noise information is suppressed through the utilization of parallel spatial and serial channel attention modules. The interactions between each module fully establish the mapping relationship between feature representation and information decision-making and improve the accuracy of lesion segmentation. Extensive experiments on three datasets of COVID-19 CXR images, and the results demonstrate that the proposed method outperforms other state-of-the-art segmentation methods of CXR images.

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