Next Article in Journal
Intuitionistic Fuzzy Three-Way Decision Model Based on the Three-Way Granular Computing Method
Previous Article in Journal
Identification of Apple Tree Leaf Diseases Based on Deep Learning Models
Open AccessArticle

Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images

College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(7), 1067; https://doi.org/10.3390/sym12071067
Received: 24 May 2020 / Revised: 21 June 2020 / Accepted: 27 June 2020 / Published: 29 June 2020
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. View Full-Text
Keywords: retinal image; blood vessel segmentation; semi-supervised learning; U-Net retinal image; blood vessel segmentation; semi-supervised learning; U-Net
Show Figures

Figure 1

MDPI and ACS Style

Chen, D.; Ao, Y.; Liu, S. Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images. Symmetry 2020, 12, 1067.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop