Difference between revisions of "Publications/morel.16.embc"
From LRDE
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| title = A Challenging Issue: Detection of White Matter Hyperintensities in Neonatal Brain MRI |
| title = A Challenging Issue: Detection of White Matter Hyperintensities in Neonatal Brain MRI |
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| booktitle = Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
| booktitle = Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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− | | pages = |
+ | | pages = 93 to 96 |
| address = Orlando, Florida, USA |
| address = Orlando, Florida, USA |
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| abstract = The progress of magnetic resonance imaging (MRI) allows for a precise exploration of the brain of premature infants at term equivalent age. The so-called DEHSI (diffuse excessive high signal intensity) of the white matter of premature brains remains a challenging issue in terms of definition, and thus of interpretation. We propose a semi-automatic detection and quantification method of white matter hyperintensities in MRI relying on morphological operators and max-tree representations, which constitutes a powerful tool to help radiologists to improve their interpretation. Results show better reproducibility and robustness than interactive segmentation. |
| abstract = The progress of magnetic resonance imaging (MRI) allows for a precise exploration of the brain of premature infants at term equivalent age. The so-called DEHSI (diffuse excessive high signal intensity) of the white matter of premature brains remains a challenging issue in terms of definition, and thus of interpretation. We propose a semi-automatic detection and quantification method of white matter hyperintensities in MRI relying on morphological operators and max-tree representations, which constitutes a powerful tool to help radiologists to improve their interpretation. Results show better reproducibility and robustness than interactive segmentation. |
Revision as of 18:57, 4 January 2018
- Authors
- Baptiste Morel, Yongchao Xu, Alessio Virzi, Thierry Géraud, Catherine Adamsbaum, Isabelle Bloch
- Where
- Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Place
- Orlando, Florida, USA
- Type
- inproceedings
- Keywords
- Image
- Date
- 2016-05-20
Abstract
The progress of magnetic resonance imaging (MRI) allows for a precise exploration of the brain of premature infants at term equivalent age. The so-called DEHSI (diffuse excessive high signal intensity) of the white matter of premature brains remains a challenging issue in terms of definition, and thus of interpretation. We propose a semi-automatic detection and quantification method of white matter hyperintensities in MRI relying on morphological operators and max-tree representations, which constitutes a powerful tool to help radiologists to improve their interpretation. Results show better reproducibility and robustness than interactive segmentation.
Documents
Bibtex (lrde.bib)
@InProceedings{ morel.16.embc, author = {Baptiste Morel and Yongchao Xu and Alessio Virzi and Thierry G\'eraud and Catherine Adamsbaum and Isabelle Bloch}, title = {A Challenging Issue: Detection of White Matter Hyperintensities in Neonatal Brain {MRI}}, booktitle = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2016}, month = aug, pages = {93--96}, address = {Orlando, Florida, USA}, abstract = {The progress of magnetic resonance imaging (MRI) allows for a precise exploration of the brain of premature infants at term equivalent age. The so-called DEHSI (diffuse excessive high signal intensity) of the white matter of premature brains remains a challenging issue in terms of definition, and thus of interpretation. We propose a semi-automatic detection and quantification method of white matter hyperintensities in MRI relying on morphological operators and max-tree representations, which constitutes a powerful tool to help radiologists to improve their interpretation. Results show better reproducibility and robustness than interactive segmentation.} }