SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
By Changlu Guo, Marton Szemenyei, Yugen Yi, Wenle Wang, Buer Chen, Changqi Fan. International Conference on Pattern Recognition, 2020
484 citations recorded by the source corpus.
The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension. In this work, we propose a lightweight network named Spatial Attention U-Net (SA-UNet) that does not require thousands of annotated training samples and can be utilized in a data augmentation manner to use the available annotated samples more efficiently. SA-UNet introduces a spatial attention module which infers the attention map along the spatial dimension, and multiplies the attention map by the input feature map for adaptive feature refinement. In addition, the proposed network employs structured dropout convolutional blocks instead of the original convolutional blocks of U-Net to prevent the network from overfitting. We evaluate SA-UNet based on two benchmark retinal datasets: the Vascular Extraction (DRIVE) dataset and the Child Heart and Health Study (CHASE_DB1) dataset. The results show that the proposed SA-UNet achieves state-of-the-art performance on both datasets. The implementation and the trained networks are available on Github11https://github.com/clguo/SA-UNet
Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/557180b3ec281593cf4d7088c735316ab3405a8b
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In Computer Vision and Pattern Recognition
- Ehsan Khodapanah Aghdam
- He-lu Huang
- Dorit Merhof
- Reza Azad
- Zuguo Chen
- Joseph Paul Cohen
- Ying Zou
- Atlas Haddadi Avval
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Channel prior convolutional attention for medical image segmentation (2023, methodology): “Important objects are the focal point of channel attention (Chen et al., 2019; Hu et al., 2018; Wang et al., 2020), whereas significant regions are the focal point of spatial attention (Guo et al., 2021; Jaderberg et al., 2015).”
- Medical Image Segmentation Review: The Success of U-Net (2022, background): “Guo et al. also add a spatial attention module to the bottleneck of their SA-UNet architecture [73].”