Retinal Vessel Segmentation based on Fully Convolutional Networks
This work addresses a critical step in diagnosing cardiovascular and ophthalmologic diseases by improving vessel segmentation accuracy, but it is incremental as it applies existing network architectures to a specific medical imaging task.
The paper tackled retinal vessel segmentation from fundus images using fully convolutional networks (U-Net and LadderNet) with patch extraction and data augmentation, achieving superior performance on public datasets like DRIVE, STARE, and CHASE_DB1 compared to recent state-of-the-art methods.
The morphological attributes of retinal vessels, such as length, width, tortuosity and branching pattern and angles, play an important role in diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases such as diabetes, hypertension and arteriosclerosis. The crucial step before extracting these morphological characteristics of retinal vessels from retinal fundus images is vessel segmentation. In this work, we propose a method for retinal vessel segmentation based on fully convolutional networks. Thousands of patches are extracted from each retinal image and then fed into the network, and data argumentation is applied by rotating extracted patches. Two architectures of fully convolutional networks, U-Net and LadderNet, are used for vessel segmentation. The performance of our method is evaluated on three public datasets: DRIVE, STARE, and CHASE\_DB1. Experimental results of our method show superior performance compared to recent state-of-the-art methods.