A Deep Learning Design for Improving Topology Coherence in Blood Vessel Segmentation

@inproceedings{Arajo2019ADL,
  title={A Deep Learning Design for Improving Topology Coherence in Blood Vessel Segmentation},
  author={Ricardo J. Ara{\'u}jo and Jaime S. Cardoso and H{\'e}lder P. Oliveira},
  booktitle={MICCAI},
  year={2019}
}
The segmentation of blood vessels in medical images has been heavily studied, given its impact in several clinical practices. Deep Learning methods have been applied to supervised segmentation of blood vessels, mainly the retinal ones due to the availability of manual annotations. Despite their success, they typically minimize the Binary Cross Entropy loss, which does not penalize topological mistakes. These errors are relevant in graph-like structures such as blood vessel trees, as a missing… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 12 REFERENCES

Beyond the Pixel-Wise Loss for Topology-Aware Delineation

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • 2017
VIEW 1 EXCERPT

A Higher-Order CRF Model for Road Network Extraction

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition
  • 2013
VIEW 1 EXCERPT