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Retinal vessel segmentation using dense U-net with multiscale inputs.

Kejuan Yue^1,2,3, Beiji Zou^1,2, Zailiang Chen^1,2

¹Central South University, School of Computer Science and Engineering, Changsha, China.

Journal of Medical Imaging (Bellingham, Wash.)

|October 2, 2019

Summary

This summary is machine-generated.

This study introduces an improved U-net model for segmenting retinal blood vessels in fundus images. The enhanced model improves the detection of thin vessels, aiding in early disease diagnosis.

Keywords:

U-net dense block multiscale retinal vessel segmentation

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Color fundus images reveal retinal vessel changes indicative of diseases like atherosclerosis and glaucoma.
Automated retinal vessel segmentation is crucial for efficient disease diagnosis.

Purpose of the Study:

To develop an improved U-net architecture for accurate retinal vessel segmentation.
To enhance the detection of subtle and thin blood vessels.

Main Methods:

An improved U-net architecture incorporating a multiscale input layer and dense blocks was proposed.
The method was evaluated on the public DRIVE dataset.

Main Results:

The proposed method achieved a sensitivity of 0.8199 and an accuracy of 0.9561 on the DRIVE dataset.
Significant improvements were observed in segmenting thin blood vessels with low contrast.

Conclusions:

The enhanced U-net architecture effectively segments retinal vessels, particularly challenging thin ones.
This automated approach can facilitate earlier and more efficient diagnosis of eye diseases.