2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDBIO-6.2
Paper Title Pyramid U-Net for Retinal Vessel Segmentation
Authors Jiawei Zhang, Fudan University, China; Yanchun Zhang, Victoria University, Australia; Xiaowei Xu, Guangdong Provincial People's Hospital, China
SessionBIO-6: Medical Image Segmentation
LocationGather.Town
Session Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Poster
Topic Biomedical Imaging and Signal Processing: [BIO-MIA] Medical image analysis
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Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Retinal blood vessel can assist doctors in diagnosis of eye-related diseases such as diabetes and hypertension, and its segmentation is particularly important for automatic retinal image analysis. However, it is challenging to segment these vessels structures, especially the thin capillaries from the color retinal image due to low contrast and ambiguousness. In this paper, we propose pyramid U-Net for accurate retinal vessel segmentation. In pyramid U-Net, the proposed pyramid-scale aggregation blocks (PSABs) are employed in both the encoder and decoder to aggregate features at higher, current and lower levels. In this way, coarse-to-fine context information is shared and aggregated in each block thus to improve the location of capillaries. To further improve performance, two optimizations including pyramid inputs enhancement and deep pyramid supervision are applied to PSABs in the encoder and decoder, respectively. For PSABs in the encoder, scaled input images are added as extra inputs. While for PSABs in the decoder, scaled intermediate outputs are supervised by the scaled segmentation labels. Extensive evaluations show that our pyramid U-Net outperforms the current state-of-the-art methods on the public DRIVE and CHASE-DB1 datasets.