Abstract
Changes in fundus blood vessels reflect the occurrence of eye diseases, and from this, we can explore other physical diseases that cause fundus lesions, such as diabetes and hypertension complication. However, the existing computational methods lack high efficiency and precision segmentation for the vascular ends and thin retina vessels. It is important to construct a reliable and quantitative automatic diagnostic method for improving the diagnosis efficiency. In this study, we propose a multichannel deep neural network for retina vessel segmentation. First, we apply U-net on original and thin (or thick) vessels for multi-objective optimization for purposively training thick and thin vessels. Then, we design a specific fusion mechanism for combining three kinds of prediction probability maps into a final binary segmentation map. Experiments show that our method can effectively improve the segmentation performances of thin blood vessels and vascular ends. It outperforms many current excellent vessel segmentation methods on three public datasets. In particular, it is pretty impressive that we achieve the best F1-score of 0.8247 on the DRIVE dataset and 0.8239 on the STARE dataset. The findings of this study have the potential for the application in an automated retinal image analysis, and it may provide a new, general, and high-performance computing framework for image segmentation.
Highlights
The fundus photography can quickly and noninvasively obtain retinal images, which is usually used as an effective way for diagnosing fundus diseases
In order to evaluate the segmentation method proposed in this article, we test on three datasets: DRIVE, STARE, and IOSTAR
To verify the robustness of our method, we perform cross-training on two datasets: DRIVE and STARE
Summary
The fundus photography can quickly and noninvasively obtain retinal images, which is usually used as an effective way for diagnosing fundus diseases. By observing retina blood vessels, medical scientists can assess symptoms of diseases, such as hypertension, diabetes, and neurodegenerative diseases. Many studies based on retinal vascular changes still rely on a manual qualitative assessment, which prevents experts from grasping retinal diseases more accurately and efficiently. Narrowed retinal blood vessels is a typical early symptom of hypertension, but disease symptoms can only be assessed subjectively by ophthalmologists through fundus photography or angiography. These early symptoms are time-consuming and hard to be spotted. A reliable and quantitative automatic diagnostic method is urgently required to improve diagnosis efficiency, and some related research works have gradually risen in the recent years
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