Enhancing Retinal Blood Vessel Segmentation through Self-Supervised Pre-Training

The segmentation of the retinal vasculature is fundamental in the study of many diseases. However, its manual completion is problematic, which motivates the research on automatic methods. Nowadays, these methods usually employ Fully Convolutional Networks (FCNs), whose success is highly conditioned by the network architecture and the availability of many annotated data, something infrequent in medicine. In this work, we present a novel application of self-supervised multimodal pre-training to enhance the retinal vasculature segmentation. The experiments with diverse FCN architectures demonstrate that, independently of the architecture, this pre-training allows one to overcome annotated data scarcity and leads to significantly better results with less training on the target task.

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M-GAN: Retinal Blood Vessel Segmentation by Balancing Losses Through Stacked Deep Fully Convolutional Networks

IEEE Access ◽

10.1109/access.2020.3015108 ◽

2020 ◽

Vol 8 ◽

pp. 146308-146322 ◽

Cited By ~ 1

Author(s):

Kyeong-Beom Park ◽

Sung Ho Choi ◽

Jae Yeol Lee

Keyword(s):

Blood Vessel ◽

Vessel Segmentation ◽

Retinal Blood Vessel ◽

Blood Vessel Segmentation ◽

Convolutional Networks ◽

Fully Convolutional Networks

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A New Deeply Convolutional Neural Network Architecture for Retinal Blood Vessel Segmentation

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421570019 ◽

2020 ◽

pp. 2157001

Author(s):

Mali Mohammedhasan ◽

Harun Uğuz

Keyword(s):

Neural Network ◽

Blood Vessel ◽

Convolutional Neural Network ◽

Blood Vessels ◽

Network Architecture ◽

Vessel Segmentation ◽

Retinal Blood Vessel ◽

Low Level ◽

Blood Vessel Segmentation ◽

Retinal Blood Vessels

This paper proposes an incoming Deep Convolutional Neural Network (CNN) architecture for segmenting retinal blood vessels automatically from fundus images. Automatic segmentation performs a substantial role in computer-aided diagnosis of retinal diseases; it is of considerable significance as eye diseases as well as some other systemic diseases give rise to perceivable pathologic changes. Retinal blood vessel segmentation is challenging because of the excessive changes in the morphology of the vessels on a noisy background. Previous deep learning-based supervised methods suffer from the insufficient use of low-level features which is advantageous in semantic segmentation tasks. The proposed architecture makes use of both high-level features and low-level features to segment retinal blood vessels. The major contribution of the proposed architecture concentrates on two important factors; the first in its supplying of extremely modularized network architecture of aggregated residual connections which enable us to copy the learned layers from the shallower model and developing additional layers to identity mapping. The second is to improve the utilization of computing resources within the network. This is achieved through a skillfully crafted design that allows for increased depth and width of the network while maintaining the stability of its computational budget. Experimental results show the effectiveness of using aggregated residual connections in segmenting retinal vessels more accurately and clearly. Compared to the best existing methods, the proposed method outperformed other existing methods in different measures, comprised less false positives at fine vessels, and caressed more clear lines with sufficient details like the human annotator.

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Retinal Blood Vessel Segmentation and Identification of Glaucoma Using Convolutional Neural Network

SSRN Electronic Journal ◽

10.2139/ssrn.3643870 ◽

2020 ◽

Author(s):

G Roopa Krishna Chandra ◽

NV Kranthi ◽

K Kavya

Keyword(s):

Neural Network ◽

Blood Vessel ◽

Convolutional Neural Network ◽

Vessel Segmentation ◽

Retinal Blood Vessel ◽

Blood Vessel Segmentation

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Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

Computational Intelligence and Neuroscience ◽

10.1155/2020/8822407 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yuliang Ma ◽

Xue Li ◽

Xiaopeng Duan ◽

Yun Peng ◽

Yingchun Zhang

Keyword(s):

Blood Vessel ◽

Blood Vessels ◽

Area Under The Curve ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Superior Performance ◽

Retinal Blood Vessel ◽

Low Contrast ◽

Blood Vessel Segmentation ◽

Small Vessels

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

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