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Blood vessel segmentation methods based on deep neural networks have achieved satisfactory results. However, these methods are usually supervised learning methods, which require large numbers of retinal images with high quality pixel-level ground-truth labels. In practice, the task of labeling these retinal images is very costly, financially and in human effort. To deal with these problems, we propose a semi-supervised learning method which can be used in blood vessel segmentation with limited labeled data. In this method, we use the improved U-Net deep learning network to segment the blood vessel tree. On this basis, we implement the U-Net network-based training dataset updating strategy. A large number of experiments are presented to analyze the segmentation performance of the proposed semi-supervised learning method. The experiment results demonstrate that the proposed methodology is able to avoid the problems of insufficient hand-labels, and achieve satisfactory performance.
Symmetry, Free Full-Text, Vrp
Symmetry, Free Full-Text
Symmetry, Free Full-Text
Symmetry, Free Full-Text
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the elements of dynamic symmetry : jay hambidge : Free Download, Borrow, and Streaming : Internet Archive
Symmetry, Free Full-Text, astd meta
Symmetry, Free Full-Text
Vector Ballandstick Model Of Titanium Dioxide Or Titania Molecule, Titanium Dioxide
A distribution-free test for symmetry in hierarchical data - CORE
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