Fully Automatic Brain Tumor Segmentation from Multiple MR Sequences using Hidden Markov Fields and Variational

@inproceedings{Menze2013FullyAB,
  title={Fully Automatic Brain Tumor Segmentation from Multiple MR Sequences using Hidden Markov Fields and Variational},
  author={Bjoern H. Menze and Mauricio Reyes and Patricia Buendia and Thomas Timothy Taylor and Michael Ryan and Nicolas Cordier and Herv{\'e} Delingette and Nicholas Ayache and EM. S. Doyle and Flor Vasseur and Michel Dojat and Florence Forbes and Joana Festa and Sergio Pereira and Jos{\'e} Antonio Mariz and Nuno Sousa and Carlos F. Silva and Xiaotao Guo and Lawrence Schwartz and Raphael Meier and Stefan Bauer and Johannes Slotboom and Roland Wiest and Shaghayegh Reza and Khan M. Iftekharuddin and Nigel John and . Liang Zhao},
  year={2013}
}
GAIN is an enhanced version of the original Grouping Artificial Immune Network that was developed for fully automated MRI brain segmentation. The model captures the main concepts by which the immune system recognizes pathogens and models the process in a numerical form. GAIN was adapted to support a variable number of input patterns for training and segmentation of tumors in MRI brain images and adapted to train on multiple images. The model was demonstrated to operate with multi-spectral MR… CONTINUE READING

Citations

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SHOWING 1-10 OF 16 CITATIONS

Efficient Interactive Brain Tumor Segmentation as Within-Brain kNN Classification

  • 2014 22nd International Conference on Pattern Recognition
  • 2014
VIEW 3 EXCERPTS
CITES BACKGROUND & METHODS
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Brain Tumor Classification: Feature Fusion

  • 2019 International Conference on Computer and Information Sciences (ICCIS)
  • 2019
VIEW 1 EXCERPT
CITES METHODS

Brain tumor segmentation on Multimodal MRI scans using EMAP Algorithm

  • 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2018

Brain tumor segmentation with Deep Neural Networks

  • Medical Image Analysis
  • 2017
VIEW 1 EXCERPT
CITES METHODS

References

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

Random Forests

  • Machine Learning
  • 2001
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

Segmentation of Brain Tumor Images Based on Integrated Hierarchical Classification and Regularization

S. Bauer, T. Fejes, +3 authors M. Reyes
  • In Proceedings of MICCAI-BRATS
  • 2012
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Classifica - tion Forests for Semantic Segmentation of Brain Lesions in Multi - channel MRI

O. Clatz, B. H. Menze, +4 authors N. Ayache
  • Decision Forests for Computer Vision and Medical Image Analysis
  • 2013

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