DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

@inproceedings{Wang2019DeepIGeoSAD,
  title={DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation},
  author={Guotai Wang and Maria A. Zuluaga and Wenqi Li and Rosalind Pratt and Premal A. Patel and Michael Aertsen and Tom Doel and Anna L. David and Jan Deprest and S{\'e}bastien Ourselin and Tom Vercauteren},
  booktitle={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2019}
}
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 66 REFERENCES

Conditional Random Fields as Recurrent Neural Networks

  • 2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL