  Automated detection of curvilinear structures, e.g., blood vessels or nerve
fibres, from medical and biomedical images is a crucial early step in automatic
image interpretation associated to the management of many diseases. Precise
measurement of the morphological changes of these curvilinear organ structures
informs clinicians for understanding the mechanism, diagnosis, and treatment of
e.g. cardiovascular, kidney, eye, lung, and neurological conditions. In this
work, we propose a generic and unified convolution neural network for the
segmentation of curvilinear structures and illustrate in several 2D/3D medical
imaging modalities. We introduce a new curvilinear structure segmentation
network (CS2-Net), which includes a self-attention mechanism in the encoder and
decoder to learn rich hierarchical representations of curvilinear structures.
Two types of attention modules - spatial attention and channel attention - are
utilized to enhance the inter-class discrimination and intra-class
responsiveness, to further integrate local features with their global
dependencies and normalization, adaptively. Furthermore, to facilitate the
segmentation of curvilinear structures in medical images, we employ a 1x3 and a
3x1 convolutional kernel to capture boundary features. ...
