Journal
2019 IEEE 19TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE)
Volume -, Issue -, Pages 439-444Publisher
IEEE
DOI: 10.1109/BIBE.2019.00085
Keywords
retinal segmentation; DropBlock; SD-Unet; U-Net; dropout
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Funding
- China Scholarship Council, China
- Stipendium Hungaricum Scholarship, Hungary
- National Natural Science Foundation of China [61602221, 61672150]
- Scientific Research Fund Project of Liaoning Provincial Department of Education [JYT19040]
- Liaoning Provincial Department of Science and Technology Natural Fund Guidance Program [2019-ZD-0234]
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At present, artificial visual diagnosis of fundus diseases has low manual reading efficiency and strong subjectivity, which easily causes false and missed detections. Automatic segmentation of retinal blood vessels in fundus images is very effective for early diagnosis of diseases such as the hypertension and diabetes. In this paper, we utilize the U-shaped structure to exploit the local features of the retinal vessels and perform retinal vessel segmentation in an end-to-end manner. Inspired by the recently DropBlock, we propose a new method called Structured Dropout U-Net (SD-U-net), which abandons the traditional dropout for convolutional layers, and applies the structured dropout to regularize U-Net. Compared to the state-of-the-art methods, we demonstrate the superior performance of the proposed approach.
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