  Analyzing the morphological attributes of blood vessels plays a critical role
in the computer-aided diagnosis of many cardiovascular and ophthalmologic
diseases. Although being extensively studied, segmentation of blood vessels,
particularly thin vessels and capillaries, remains challenging mainly due to
the lack of an effective interaction between local and global features. In this
paper, we propose a novel deep learning model called PC-Net to segment retinal
vessels and major arteries in 2D fundus image and 3D computed tomography
angiography (CTA) scans, respectively. In PC-Net, the pyramid
squeeze-and-excitation (PSE) module introduces spatial information to each
convolutional block, boosting its ability to extract more effective multi-scale
features, and the coarse-to-fine (CF) module replaces the conventional decoder
to enhance the details of thin vessels and process hard-to-classify pixels
again. We evaluated our PC-Net on the Digital Retinal Images for Vessel
Extraction (DRIVE) database and an in-house 3D major artery (3MA) database
against several recent methods. Our results not only demonstrate the
effectiveness of the proposed PSE module and CF module, but also suggest that
our proposed PC-Net sets new state of the art in the segmentation of retinal
vessels (AUC: 98.31%) and major arteries (AUC: 98.35%) on both databases,
respectively.
