CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging

@article{Mou2020CS2NetDL,
  title={CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging},
  author={Lei Mou and Yitian Zhao and H. Fu and Yonghuai Liu and Jun Cheng and Yalin Zheng and Pan Su and Jianlong Yang and L. Chen and Alejandro F Frangi and Masahiro Akiba and Jiang Liu},
  journal={Medical image analysis},
  year={2020},
  volume={67},
  pages={
          101874
        }
}
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81 Citations
Highly Influential Citations
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Background Citations
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81 Citations

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References

SHOWING 1-10 OF 85 REFERENCES

CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation

This work proposes a general unifying curvilinear structure segmentation network that works on different medical imaging modalities: optical coherence tomography angiography, color fundus image, and corneal confocal microscopy, and instead of the U-Net based convolutional neural network, a novel network which includes a self-attention mechanism in the encoder and decoder.

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

Comprehensive results show that the proposed CE-Net method outperforms the original U- net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation , cell contour segmentation and retinal optical coherence tomography layer segmentation.

Multi-level deep supervised networks for retinal vessel segmentation

  • Juan MoLei Zhang
  • Medicine, Computer Science
    International Journal of Computer Assisted Radiology and Surgery
  • 2017
The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks, making it suitable for real-world clinical applications.

DUNet: A deformable network for retinal vessel segmentation

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

This work proposes an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network, trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once.

Deep Vessel Segmentation By Learning Graphical Connectivity

U-Net: Convolutional Networks for Biomedical Image Segmentation

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

This paper presents UNet++, a new, more powerful architecture for medical image segmentation where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways, and argues that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar.

Cerebrovascular Network Segmentation of MRA Images With Deep Learning

This work presents a convolutional neural network approach for segmentation of the cerebrovascular structure from magnetic resonance angiography inspired by the U-net 3D and by the Inception modules, entitled Uception.

Attention U-Net: Learning Where to Look for the Pancreas

A novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes is proposed to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs).
...