View source for Publications/xu.18.media

{{Publication
| published = true
| date = 2018-03-22
| authors = Yongchao Xu, Baptiste Morel, Sonia Dahdouh, Élodie Puybareau, Alessio Virzì, Hélène Urien,  Thierry&nbsp;Géraud, Catherine Adamsbaum, Isabelle Bloch
| title = The Challenge of Cerebral Magnetic Resonance Imaging in Neonates: A New Method using Mathematical Morphology for the Segmentation of Structures Including Diffuse Excessive High Signal Intensities
| journal = Medical Image Analysis
| pages = 75 to 94
| volume = 48
| lrdeprojects = Olena
| abstract = Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value. The aim of this paper is to propose a new semi-automated framework to detect DEHSI on neonatal brain MR images having a particular pattern due to the physiological lack of complete myelination of the white matter. A novel method for semi- automatic segmentation of neonatal brain structures and DEHSI, based on mathematical morphology and on max-tree representations of the images is thus described. It is a mandatory first step to identify and clinically assess homogeneous cohorts of neonates for DEHSI and/or volume of any other segmented structures. Implemented in a user-friendly interface, the method makes it straightforward to select relevant markers of structures to be segmented, and if needed, apply eventually manual corrections. This method responds to the increasing need for providing medical experts with semi-automatic tools for image analysis, and overcomes the limitations of visual analysis alone, prone to subjectivity and variability. Experimental results demonstrate that the method is accurate, with excellent reproducibility and with very few manual corrections needed. Although the method was intended initially for images acquired at 1.5T, which corresponds to usual clinical practice, preliminary results on images acquired at 3T suggest that the proposed approach can be generalized.
| lrdepaper = http://www.lrde.epita.fr/dload/papers/xu.18.media.pdf
| lrdeinc = Publications/xu.18.media.inc
| lrdenewsdate = 2018-03-22
| lrdekeywords = Image
| type = article
| id = xu.18.media
| bibtex = 
@Article<nowiki>{</nowiki>	  xu.18.media,
  author	= <nowiki>{</nowiki>Yongchao Xu and Baptiste Morel and Sonia Dahdouh and
		  \'Elodie Puybareau and Alessio Virz\`i and H\'el\`ene Urien
		  and Thierry~G\'eraud and Catherine Adamsbaum and Isabelle
		  Bloch<nowiki>}</nowiki>,
  title		= <nowiki>{</nowiki>The Challenge of Cerebral Magnetic Resonance Imaging in
		  Neonates: <nowiki>{</nowiki>A<nowiki>}</nowiki> New Method using Mathematical Morphology for
		  the Segmentation of Structures Including Diffuse Excessive
		  High Signal Intensities<nowiki>}</nowiki>,
  journal	= <nowiki>{</nowiki>Medical Image Analysis<nowiki>}</nowiki>,
  year		= 2018,
  month		= aug,
  pages		= <nowiki>{</nowiki>75--94<nowiki>}</nowiki>,
  volume	= <nowiki>{</nowiki>48<nowiki>}</nowiki>,
  abstract	= <nowiki>{</nowiki>Preterm birth is a multifactorial condition associated
		  with increased morbidity and mortality. Diffuse excessive
		  high signal intensity (DEHSI) has been recently described
		  on T2-weighted MR sequences in this population and thought
		  to be associated with neuropathologies. To date, no robust
		  and reproducible method to assess the presence of white
		  matter hyperintensities has been developed, perhaps
		  explaining the current controversy over their prognostic
		  value. The aim of this paper is to propose a new
		  semi-automated framework to detect DEHSI on neonatal brain
		  MR images having a particular pattern due to the
		  physiological lack of complete myelination of the white
		  matter. A novel method for semi- automatic segmentation of
		  neonatal brain structures and DEHSI, based on mathematical
		  morphology and on max-tree representations of the images is
		  thus described. It is a mandatory first step to identify
		  and clinically assess homogeneous cohorts of neonates for
		  DEHSI and/or volume of any other segmented structures.
		  Implemented in a user-friendly interface, the method makes
		  it straightforward to select relevant markers of structures
		  to be segmented, and if needed, apply eventually manual
		  corrections. This method responds to the increasing need
		  for providing medical experts with semi-automatic tools for
		  image analysis, and overcomes the limitations of visual
		  analysis alone, prone to subjectivity and variability.
		  Experimental results demonstrate that the method is
		  accurate, with excellent reproducibility and with very few
		  manual corrections needed. Although the method was intended
		  initially for images acquired at 1.5T, which corresponds to
		  usual clinical practice, preliminary results on images
		  acquired at 3T suggest that the proposed approach can be
		  generalized.<nowiki>}</nowiki>
<nowiki>}</nowiki>

}}