Residues in Succession U-Net for Fast and Efficient Segmentation

Date of Award


Degree Name

M.S. in Electrical and Computer Engineering


Department of Electrical and Computer Engineering


Vijayan Asari


Vascular network of human eye plays an important diagnostic role in ophthalmology. Various size of the vessels, relatively low contrast and the presence of potential retinal diseases or pathologies complicate the segmentation process in fundus imaging. It is impossible to segment the vascular ends of thin retina vessels with the existing computational methods due to their high inefficiency and precision. Deep learning provides superior performance in semantic segmentation, especially for biomedical applications. One of the popular deep learning architectures for semantic segmentation is U-Net, which is specifically tailored for feature cascading to perform effective pixel classification. Advanced versions of U-Net such as Recurrent U-Net (RU-Net) and Recurrent Residual U-Net (R2U-Net) had been proposed for improved performance. The studies state that learning from a significant depth and extensive network with residual units is more accurate and can extract more discriminative feature representation for segmentation than learning from a shallow network without the residual units. In other words, residual learning reinforces the features in the previous layers to extract more versatile characteristics. It is observed that the reinforcement of features in successive layers would provide a relatively faster and efficient performance in image segmentation. In this thesis, we propose a modified U- Net architecture incorporating the residues from successive layers for the extraction of features in subsequent layers. The new model, named as Residues in Succession U-Net, is optimized for better overall performance exhibiting qualitative and quantitative results with the same number of parameters. 4 The Residues in Succession U-Net is evaluated for blood vessel segmentation in retinal images on a benchmark expert-annotated dataset viz. Structured Analysis of Retina (STARE). The testing and evaluation results show that the new model provides improved performance when compared to U-Net and R2U-Net in the same experimentation setup. Additionally, the model is evaluated on preprocessed image data with a nonlinear image enhancement strategy, known as Integrated Neighborhood Dependent Approach for Nonlinear Enhancement of color images (AINDANE) to improve the fine details in the images. However, it is observed that the enhancement process boosted the noise components too which reduced the quality of learning performance of the network. Residues in succession U-Net evaluated on original data produced superior quantitative results when compared with other U-Net models. We are considering the application of a sophisticated enhancement strategy and use of a more effective loss function to improve the segmentation performance.


Computer Engineering, Computer Science, Artificial Intelligence, Biomedical Research, residues in succession U-Net, efficient segmentation, residues in succession, subsequent layers, residual in subsequent layers, encoder, decoder, U-Net, segmentation for biomedical dataset, retinal blood vessel segmentation, precise segmentation for retinal blood vessels

Rights Statement

Copyright © 2022, author.