ArticleFrontiers in medicine2023
Improved fully convolutional neuron networks on small retinal vessel segmentation using local phase as attention.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 4 citations in OpenAlex.
- Image harmonization and de-harmonization based on singular value decomposition (SVD) in medical domain.Quantitative imaging in medicine and surgery · 2025Article
- Joint 2D-3D cross-pseudo supervision for carotid vessel wall segmentation.Frontiers in cardiovascular medicine · 2023Article
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8 authors at 6 institutions in 5 countries.
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Abstract
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 (
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