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

@article{Milletari2016VNetFC,
  title={V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation},
  author={Fausto Milletari and Nassir Navab and Seyed-Ahmad Ahmadi},
  journal={2016 Fourth International Conference on 3D Vision (3DV)},
  year={2016},
  pages={565-571}
}
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 20 REFERENCES

U-Net: Convolutional Networks for Biomedical Image Segmentation

  • Bildverarbeitung für die Medizin
  • 2017
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Deep Residual Learning for Image Recognition

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
VIEW 1 EXCERPT

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