Improved fully convolutional neuron networks on small retinal vessel segmentation using local phase as attention

Retinal images have been proven significant in diagnosing multiple diseases such as diabetes, glaucoma, and hypertension. Retinal vessel segmentation is crucial for the quantitative analysis of retinal images. However, current methods mainly concentrate on the segmentation performance of overall retinal vessel structures. The small vessels do not receive enough attention due to their small percentage in the full retinal images. Small retinal vessels are much more sensitive to the blood circulation system and have great significance in the early diagnosis and warning of various diseases. This paper combined two unsupervised methods, local phase congruency (LPC) and orientation scores (OS), with a deep learning network based on the U-Net as attention. And we proposed the U-Net using local phase congruency and orientation scores (UN-LPCOS), which showed a remarkable ability to identify and segment small retinal vessels. A new metric called sensitivity on a small ship (Se-sv) was also proposed to evaluate the methods' performance on the small vessel segmentation. Our strategy was validated on both the DRIVE dataset and the data from Maastricht Study and achieved outstanding segmentation performance on both the overall vessel structure and small vessels.

Automated summary

Retinal images are important for diagnosing diseases. Segmentation of retinal vessels is crucial for the analysis of retinal images. Current methods focus on overall vessel structures and neglect small vessels. This paper proposes a method (UN-LPCOS) that combines unsupervised methods with a deep learning network to segment small retinal vessels effectively. A new metric (Se-sv) is also introduced to evaluate the segmentation performance. The proposed strategy achieves outstanding results on both overall vessel structure and small vessels.