Deep Encoder–Decoder Neural Networks for Retinal Blood Vessels Dense Prediction

Authors

Wenlu Zhang^{1, *}, Lusi Li², Vincent Cheong^1, , Bo Fu¹, Mehrdad Aliasgari¹

¹Department of Computer Engineering and Computer Science, California State University Long Beach, 1250 Bellflower Blvd. Long Beach, California, 90840, USA

²Department of Computer Information Systems, California State University Los Angels, 5151 State University Dr. Los Angeles, California, 90032, USA

^*Corresponding author. Email: wenlu.zhang@csulb.edu

DOI

10.2991/ijcis.d.210308.001How to use a DOI?

Keywords

Deep learning; Encoder-decoder; Retinal blood vessel; Dense prediction

Abstract

Automatic segmentation of retinal blood vessels from fundus images is of great importance in assessing the condition of vascular network in human eyes. The task is primary challenging due to the low contrast of images, the variety of vessels and potential pathology. Previous studies have proposed shallow machine learning based methods to tackle the problem. However, these methods require specific domain knowledge, and the efficiency and robustness of these methods are not satisfactory for medical diagnosis. In recent years, deep learning models have made great progress in various segmentation tasks. In particular, Fully Convolutional Network and U-net have achieved promising results in end-to-end dense prediction tasks. In this study, we propose a novel encoder-decoder architecture based on the vanilla U-net architecture for retinal blood vessels segmentation. The proposed deep learning architecture integrates hybrid dilation convolutions and pixel transposed convolutions in the encoder-decoder model. Such design enables global dense feature extraction and resolves the common “gridding” and “checkerboard” issues in the regular U-net. Furthermore, the proposed network can be efficiently and directly implemented for any semantic segmentation applications. We evaluate the proposed network on two retinal blood vessels data sets. The experimental results show that our proposed model outperforms the baseline U-net model.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Wenlu Zhang
AU  - Lusi Li
AU  - Vincent Cheong
AU  - Bo Fu
AU  - Mehrdad Aliasgari
PY  - 2021
DA  - 2021/03/22
TI  - Deep Encoder–Decoder Neural Networks for Retinal Blood Vessels Dense Prediction
JO  - International Journal of Computational Intelligence Systems
SP  - 1078
EP  - 1086
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210308.001
DO  - 10.2991/ijcis.d.210308.001
ID  - Zhang2021
ER  -