DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

@article{Wang2018DeepIGeoSAD,
  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},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2018}
}
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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