Publications/morel.16.embc

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

• Paper

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.}
}