DUNet: A deformable network for retinal vessel segmentation

By Qiangguo Jin, Zhao-Peng Meng, Tuan D. Pham, Qi Chen, Leyi Wei, Ran Su. Knowledge-Based Systems, 2018

819 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/6fadd5b08391b0fd0b9bf9f6c18b2dc3194907b9

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Citations in the corpus

Medical Image Segmentation Review: The Success of U-Net (2022, methodology): “[65] exchange each 3 × 3 convolutional layer in the original U-Net with a deformable convolutional block for the accurate segmentation of retinal vessels.”
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, methodology): “Images have been collected from papers in this survey, including: (a) Retinal vessel segmentation [107]; (b) Brain tumor detection and segmentation [108]; (c) Multi-organ abdominal segmentation (liver; spleen; left and right kidneys; pancreas; gallbladder; aorta; and inferior vena cava) on CT scans ”
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks (2019, background): “Only the results of UNet, DenseBlock-UNet, and Deform-UNet were from our reproduced tests, while all other results were adopted from the corresponding papers.”
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation (2020, neutral): “By segmenting the retinal blood vessels, we can obtain the relevant morphological information of the retinal blood vessel tree (such as the curvature, length, and width of the blood vessels) [3].”