Encoder Enhanced Atrous (EEA) Unet architecture for Retinal Blood vessel segmentation |
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| Institution: | 1. Federal University of the Valleys of Jequitinhonha and Mucury, Diamantina, MG, Brazil;2. University of Sao Paulo, Brazil;1. Department of Computer Science and Technology, Central South University, Changsha, Hunan 410083, China;2. Centre for Medical Image Computing, University College London, London WC1V 6LJ, United Kingdom;3. Changsha Aier Eye Hospital, Changsha, Hunan 410015, China;1. Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran;2. Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran;1. School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China;2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China;3. University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | The retinal blood vessel segmentation is required for continuously monitoring the blood vessel in most of the retinal disease diagnosis. Deep learning approaches are accepted as the promising techniques for biomedical image segmentation. In this paper, Encoder enhanced Atrous architecture is proposed for retinal blood vessel segmentation. The encoder section is enhanced by improving the depth concatenation process with the addition layers. The proposed architecture is evaluated on the publicly available databases DRIVE, STARE, CHASE_DB1 and HRF using metrics like accuracy, sensitivity, specificity, Dice coefficient, and Mathew’s correlation coefficient. The proposed architecture performs better compared to the conventional Unet architecture in terms of accuracy by 0.35% and 0.83% for DRIVE and STARE respectively. In terms of specificity and Dice score, the proposed architecture also shows improved results compared to the Unet architecture. |
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| Keywords: | Enhanced encoder Atrous Vessel segmentation Unet |
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