Contextual Information Enhanced Convolutional Neural Networks for Retinal Vessel Segmentation in Color Fundus Images
This work addresses the challenge of accurate retinal vessel segmentation to facilitate clinical diagnosis and ophthalmological research, representing an incremental improvement over existing methods.
The paper tackled the problem of retinal vessel segmentation in color fundus images by proposing a deep learning method that integrates cascaded dilated convolutional modules and pyramid modules into a U-net architecture, achieving state-of-the-art performance in Sensitivity/Recall, F1-score, and MCC on datasets like DRIVE, CHASEDB1, and STARE.
Accurate retinal vessel segmentation is a challenging problem in color fundus image analysis. An automatic retinal vessel segmentation system can effectively facilitate clinical diagnosis and ophthalmological research. Technically, this problem suffers from various degrees of vessel thickness, perception of details, and contextual feature fusion. For addressing these challenges, a deep learning based method has been proposed and several customized modules have been integrated into the well-known encoder-decoder architecture U-net, which is mainly employed in medical image segmentation. Structurally, cascaded dilated convolutional modules have been integrated into the intermediate layers, for obtaining larger receptive field and generating denser encoded feature maps. Also, the advantages of the pyramid module with spatial continuity have been taken, for multi-thickness perception, detail refinement, and contextual feature fusion. Additionally, the effectiveness of different normalization approaches has been discussed in network training for different datasets with specific properties. Experimentally, sufficient comparative experiments have been enforced on three retinal vessel segmentation datasets, DRIVE, CHASEDB1, and the unhealthy dataset STARE. As a result, the proposed method outperforms the work of predecessors and achieves state-of-the-art performance in Sensitivity/Recall, F1-score and MCC.