Computer-Aided Medical Image Annotation: Preliminary Results With Liver Lesions in CT

@article{Marvasti2018ComputerAidedMI,
  title={Computer-Aided Medical Image Annotation: Preliminary Results With Liver Lesions in CT},
  author={Neda Barzegar Marvasti and Erdem Y{\"o}r{\"u}k and Burak Acar},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2018},
  volume={22},
  pages={1561-1570}
}
The increasing volume of medical image data, as well as the need for multicenter data consolidation for big data analytics, require computer-aided medical image annotation (CMIA). Majority of the methods proposed so far do not exploit interdependencies between annotations explicitly. They further limit their annotations at a higher level than diagnostics and/or do not consider a standardized lexicon. A radiologist-in-the-loop semi-automatic CMIA system is proposed. It is based on a Bayesian… CONTINUE READING

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Key Quantitative Results

  • The experiments have shown that on the average 7.50 manual annotations are sufficient to reach 95% accuracy which corresponds to 3.7-fold speed-up over manual annotation alone.

References

Publications referenced by this paper.
SHOWING 1-10 OF 43 REFERENCES

Semantic Description of Liver CT Images: An Ontological Approach

  • IEEE Journal of Biomedical and Health Informatics
  • 2014
VIEW 7 EXCERPTS

Introducing Geometry in Active Learning for Image Segmentation

  • 2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
VIEW 1 EXCERPT