Ms. Xiaoyu Zou Profile

Xiaoyu Zou

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Proceedings Article | 13 June 2024 Paper

MDF-Net: multiscale deep fusion network and dual threshold decision for retinal vessel segmentation

Qiyuan Xin, Liying Han, Xiaoyu Zou

Proceedings Volume 13180, 131806O (2024) https://doi.org/10.1117/12.3033747

KEYWORDS: Tunable filters, Image segmentation, Education and training, Convolution, Image enhancement, Blood vessels, Image fusion, Feature extraction

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Accurate and automated retinal vessel segmentation is crucial for the diagnosis and prevention of many diseases. However, current methods still fall short in processing vessels of different scales, especially in the case of fine vessels. To solve the above issues, this paper proposes a multiscale deep fusion network for retinal vessel segmentation. Specifically, this paper first designs a Frangi enhancement module. The FE module leverages the output response from the Frangi filter in combination with the feature map of the neural network, which assists the network in segmenting more fine blood vessels. Secondly, this paper proposes a multiscale bidirectional fusion (MBF) module. The MBF module comprises dilated convolutions with different dilation rates and bidirectional attention mechanisms, designed to extract vascular features at various scales and establish connections between distant pixels. Finally, this paper investigates a dual-threshold decision (DTD) algorithm, which is used to solve the problem of ambiguous pixel determination near the single threshold. The proposed MDF-Net is evaluated on three typical fundus image datasets (DRIVE, STARE, CHASE-DB1), and the experimental results indicate that MDF-Net exhibits superior performance compared to other state-of-the-art methods. Additionally, the application of the DTD algorithm significantly improves the SE (0.8615, 0.8804, 0.8792) metric, effectively enhancing the connectivity of the vessels.

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