Journal
IEEE ACCESS
Volume 8, Issue -, Pages 116106-116116Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3002835
Keywords
Image segmentation; Biomedical optical imaging; Optical imaging; Optical filters; Retina; Adaptive optics; Biomedical image segmentation; semantic segmentation; guided filter
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Funding
- National Natural Science Foundation of China (NSFC) [61876208]
- Guangdong Provincial Scientific and Technological funds [2017B090901008, 2018B010108002]
- Pearl River S&T Nova Program of Guangzhou [201806010081]
- Natural Science Foundation of Guangdong Province of China [2017A030310647]
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Segmentation of 2D images is a fundamental problem for biomedical image analysis. The most widely used architecture for biomedical image segmentation is U-Net. U-Net introduces skip-connections to restore the spatial information loss caused by down-sampling operations. However, for some tasks such as the retinal vessel segmentation, the loss information of structure can not be fully recovered since the vessels is merely a curve line that can not be detected after several convolutions. In this paper, we introduce a deep guidance network to segment the biomedical image. Our proposed network consists of a guided image filter module to restore the structure information through the guidance image. Our method enables end to end training and fast inference (43ms for one image). We conduct extensive experiments for the task of vessel segmentation and optic disc and cup segmentation. The experiments on four publicly available datasets: ORIGA, REFUGE, DRIVE, and CHASEDB1 verify the effectiveness of our method.
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