SuperVessel: Segmenting High-Resolution Vessel from Low-Resolution Retinal Image
The 5th Chinese Conference on Pattern Recognition and Computer Vision
Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases. Ophthalmologists often require high-resolution segmentation results for analysis, which leads to super-computational load by most existing methods. If based on low-resolution input, they easily ignore tiny vessels or cause discontinuity of segmented vessels. To solve these problems, the paper proposes an algorithm named SuperVessel, which gives out high-resolution and accurate vessel segmentation using low-resolution images as input. We first take super-resolution as our auxiliary branch to provide potential high-resolution detail features, which can be deleted in the test phase. Secondly, we propose two modules to enhance the features of the interested segmentation region, including an upsampling with feature decomposition (UFD) module and a feature interaction module (FIM) with a constraining loss to focus on the interested features. Extensive experiments on three publicly available datasets demonstrate that our proposed SuperVessel can segment more tiny vessels with higher segmentation accuracy IoU over 6\%, compared with other state-of-the-art algorithms. Besides, the stability of SuperVessel is also stronger than other algorithms. The code will be released at https://github.com/Qsingle/Megvision.
Cited Times [WOS]:0
|Document Type||Conference paper|
|Department||Research Institute of Trustworthy Autonomous Systems|
1.Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Technology, Southern University of Science and Technology
2.Department of Ophthalmology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University
3.The First Affiliated Hospital, Southern University of Science and Technology
4.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology
Yan,Hu,Zhongxi,Qiu,Dan,Zeng,et al. SuperVessel: Segmenting High-Resolution Vessel from Low-Resolution Retinal Image[C]:Springer Nature Switzerland,2022.
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