Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation

@inproceedings{Yoo2014DeepLO,
  title={Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation},
  author={Youngjin Yoo and Tom Brosch and Anthony Traboulsee and David K. B. Li and Roger C. Tam},
  booktitle={MLMI},
  year={2014}
}
A new automatic method for multiple sclerosis (MS) lesion segmentation in multi-channel 3D MR images is presented. The main novelty of the method is that it learns the spatial image features needed for training a supervised classifier entirely from unlabeled data. This is in contrast to other current supervised methods, which typically require the user to preselect or design the features to be used. Our method can learn an extensive set of image features with minimal user effort and bias. In… CONTINUE READING
Highly Cited
This paper has 28 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 12 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 14 references

An automatic segmentation of T2-FLAIR multiple sclerosis lesions

  • Souplet, J.C
  • MS Lesion Segmentation Challenge, MICCAI Workshop
  • 2008
Highly Influential
4 Excerpts

Automatic segmentation of MS lesions using a contextual model for the MICCAI grand challenge

  • J Morra
  • MS Lesion Segmentation Challenge (MICCAI Workshop…
  • 2008
1 Excerpt

Similar Papers

Loading similar papers…