4.7 Article

Retinal Vessel Segmentation Algorithm Based on Residual Convolution Neural Network

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fbioe.2021.786425

关键词

retinal vessel segmentation; convolution neural network (CNN); residual network; fundus image; attentional mechanism; deep supervision

资金

  1. National Natural Science Foundation of China [51905393, 12102311]
  2. Wuhan Zhaoyang Youth Science and Technology Project [202004]
  3. Hubei provincial Chenguang Youth Science and Technology Project [2019B08]
  4. program of China Scholarships Council [201808420326]

向作者/读者索取更多资源

The retinal vessel segmentation algorithm based on residual convolution neural network is able to accurately identify retinal vessels in fundus images, with excellent performance in achieving complete retinal vessel segmentation, connected vessel stems and terminals. The algorithm has proven to be effective in detecting more capillaries, with superior accuracy and specificity compared to other methods.
Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90 and 96.88%, and the average specificity is 98.85 and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.

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