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Dense U-net Based on Patch-Based Learning for Retinal Vessel Segmentation.

Chang Wang¹, Zongya Zhao¹, Qiongqiong Ren¹

¹School of Biomedical Engineering, Xinxiang Medical University, Xinxiang City Engineering Technology Research Center of Neurosensor and Control, Xinxiang 453003, China.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

This summary is machine-generated.

This study introduces a novel retinal vessel segmentation framework using Dense U-net and patch-based learning. The method achieves competitive results, outperforming specialists in accuracy and clinical application value.

Keywords:

Dense U-net Retinal vessel segmentation convolutional neural network data augmentation patch-based learning strategy

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Area of Science:

Medical Imaging
Computer Vision
Ophthalmology

Background:

Retinal vessel segmentation is crucial for diagnosing eye diseases.
Existing methods, including convolutional neural networks (CNNs), face challenges with tiny vessel features.
Dense U-net shows promise for semantic segmentation tasks.

Purpose of the Study:

To develop an advanced retinal vessel segmentation framework.
To leverage Dense U-net and a patch-based learning strategy for improved feature extraction.
To enhance the accuracy and clinical applicability of automated retinal vessel segmentation.

Main Methods:

A novel framework combining Dense U-net with a patch-based learning strategy was proposed.
Training involved random patch extraction, Dense U-net, and data augmentation via random transformations.
Testing utilized an overlapping-patches sequential reconstruction strategy for segmentation.

Main Results:

The proposed method was evaluated on the DRIVE and STARE datasets.
Performance metrics included sensitivity, specificity, accuracy, and AUC.
The approach demonstrated competitive performance against state-of-the-art unsupervised, supervised, and CNN methods.

Conclusions:

The developed framework offers a robust solution for retinal vessel segmentation.
The method achieves superior segmentation results compared to specialists.
The approach holds significant clinical application value for ophthalmology.