SSCA-Net: Simultaneous Self- and Channel-Attention Neural Network for Multiscale Structure-Preserving Vessel Segmentation.Vessel segmentation is a fundamental, yet not well-solved problem in medical image analysis, due to the complicated geometrical and topological structures of human vessels.  Unlike existing rule- and conventional learning-based techniques, which hardly capture the location of tiny vessel structures and perceive their global spatial structures, we propose Simultaneous Self- and Channel-attention Neural Network (termed SSCA-Net) to solve the multiscale structure-preserving vessel segmentation (MSVS) problem.  SSCA-Net differs from the conventional neural networks in modeling image global contexts, showing more power to understand the global semantic information by both self- and channel-attention (SCA) mechanism and offering high performance on segmenting vessels with multiscale structures (e.g., DSC: 96.21% and MIoU: 92.70% on the intracranial vessel dataset).  To evaluate the effectiveness of our SSCA-Net, we compare it with several state-of-the-art methods on three well-known vessel segmentation benchmark datasets. 
SSCA-Net
SSCA-Net: Simultaneous Self- and Channel-Attention Neural Network for Multiscale Structure-Preserving Vessel Segmentation.Vessel segmentation is a fundamental, yet not well-solved problem in medical image analysis, due to the complicated geometrical and topological structures of human vessels.  Unlike existing rule- and conventional learning-based techniques, which hardly capture the location of tiny vessel structures and perceive their global spatial structures, we propose Simultaneous Self- and Channel-attention Neural Network (termed SSCA-Net) to solve the multiscale structure-preserving vessel segmentation (MSVS) problem. 
Simultaneous Self- and Channel-attention Neural Network
Unlike existing rule- and conventional learning-based techniques, which hardly capture the location of tiny vessel structures and perceive their global spatial structures, we propose Simultaneous Self- and Channel-attention Neural Network (termed SSCA-Net) to solve the multiscale structure-preserving vessel segmentation (MSVS) problem. 
MSVS
SSCA-Net: Simultaneous Self- and Channel-Attention Neural Network for Multiscale Structure-Preserving Vessel Segmentation.Vessel segmentation is a fundamental, yet not well-solved problem in medical image analysis, due to the complicated geometrical and topological structures of human vessels.  Unlike existing rule- and conventional learning-based techniques, which hardly capture the location of tiny vessel structures and perceive their global spatial structures, we propose Simultaneous Self- and Channel-attention Neural Network (termed SSCA-Net) to solve the multiscale structure-preserving vessel segmentation (MSVS) problem. 
Multiscale Structure-Preserving Vessel Segmentation