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ArXiv Preprint

Retinal vessels are important biomarkers for many ophthalmological and cardiovascular diseases. It is of great significance to develop an accurate and fast vessel segmentation model for computer-aided diagnosis. Existing methods, such as U-Net follows the encoder-decoder pipeline, where detailed information is lost in the encoder in order to achieve a large field of view. Although detailed information could be recovered in the decoder via multi-scale fusion, it still contains noise. In this paper, we propose a deep segmentation model, called detail-preserving network (DPN) for efficient vessel segmentation. To preserve detailed spatial information and learn structural information at the same time, we designed the detail-preserving block (DP-Block). Further, we stacked eight DP-Blocks together to form the DPN. More importantly, there are no down-sampling operations among these blocks. As a result, the DPN could maintain a high resolution during the processing, which is helpful to locate the boundaries of thin vessels. To illustrate the effectiveness of our method, we conducted experiments over three public datasets. Experimental results show, compared to state-of-the-art methods, our method shows competitive/better performance in terms of segmentation accuracy, segmentation speed, extensibility and the number of parameters. Specifically, 1) the AUC of our method ranks first/second/third on the STARE/CHASE_DB1/DRIVE datasets, respectively. 2) Only one forward pass is required of our method to generate a vessel segmentation map, and the segmentation speed of our method is over 20-160x faster than other methods on the DRIVE dataset. 3) We conducted cross-training experiments to demonstrate the extensibility of our method, and results revealed that our method shows superior performance. 4) The number of parameters of our method is only around 96k, less then all comparison methods.

Song Guo