DUNet: A deformable network for retinal vessel segmentation
TLDR
Wang et al. as discussed by the authors proposed Deformable U-Net (DUNet), which exploits the retinal vessels' local features with a U-shape architecture, in an end-to-end manner for retinal vessel segmentation.Abstract:
Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels’ local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level features with high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels’ scales and shapes. Public datasets: DRIVE, STARE, CHASE_DB1 and HRF are used to test our models. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels can be extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9566/0.9641/0.9610/0.9651 and AUC of 0.9802/0.9832/0.9804/0.9831 on DRIVE, STARE, CHASE_DB1 and HRF respectively. Moreover, to show the generalization ability of the DUNet, we use another two retinal vessel data sets, i.e., WIDE and SYNTHE, to qualitatively and quantitatively analyze and compare with other methods. Extensive cross-training evaluations are used to further assess the extendibility of DUNet. The proposed method has the potential to be applied to the early diagnosis of diseases.read more
Citations
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Journal ArticleDOI
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications
TL;DR: A narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends, and discusses the many innovations that have advanced in deep learning and how these tools facilitate U-nets.
Journal ArticleDOI
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
TL;DR: This work proposes a 3D hybrid residual attention-aware segmentation method, i.e., RA-UNet, to precisely extract the liver region and segment tumors from the liver.
Proceedings ArticleDOI
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
TL;DR: This work proposes IterNet, a new model based on UNet, with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image, to improve the performance of vessel segmentation.
Journal ArticleDOI
CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging.
Lei Mou,Yitian Zhao,Huazhu Fu,Yonghuai Liu,Jun Cheng,Yalin Zheng,Pan Su,Jianlong Yang,Li Chen,Alejandro F. Frangi,Masahiro Akiba,Jiang Liu,Jiang Liu +12 more
TL;DR: Wang et al. as discussed by the authors proposed a self-attention mechanism in the encoder and decoder to learn rich hierarchical representations of curvilinear structures and employed a 1×3 and a 3×1 convolutional kernel to capture boundary features.
Journal ArticleDOI
ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model
Yuhui Ma,Huaying Hao,Jianyang Xie,Huazhu Fu,Jiong Zhang,Jianlong Yang,Zhen Wang,Jiang Liu,Yalin Zheng,Yitian Zhao +9 more
TL;DR: In this article, a split-based coarse-to-fine vessel segmentation network for OCTA images (OCTA-Net) was proposed, with the ability to detect thick and thin vessels separately.
References
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Posted Content
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