Multi-channel conditional generative adversarial networks retinal vessel segmentation algorithm

Objective

To propose a model for accurately segmenting blood vessels in medical fundus images.

Methods

The algorithm of deep learning was used for the task of automatic segmentation of blood vessels in retinal fundus images in this paper.An improved vascular segmentation algorithm was proposed.For the different types of blood vessels in the fundus image, a multi-scale network structure was designed to extract features of both main blood vessels and vessel branches at the same time.

Results

The segmentation model proposed could achieve good results on all kinds of blood vessels even if they have low contrast and few obvious characteristics.The automatic vessel segmentation of retinal fundus images was implemented, and the performance of the model was evaluated through multiple evaluation indexes which are widely used in the field of medical image segmentation in the test stage.A specificity of 0.982 9, an F1 score of 0.794 4, a G-mean of 0.874 8, an Matthews correlation coefficient(MCC) of 0.776 4 and a specificity of 0.978 2 were obtained on the DRIVE dataset.An F1 score of 0.773 5 and an MCC of 0.757 3 were obtained on the STARE data set.

Conclusions

The proposed method has a great improvement over the segmentation algorithm of the same task.Furthermore, the results generated by our model can achieve comparable effect with the segmentation of human doctor.

Retinal fundus images; Vessel segmentation; Medical image processing; Deep learning; Conditional generative adversarial networks