Difference between revisions of "NeoBrainSeg"

From LRDE

(Created page with "== The Challenge of Cerebral MRI in Neonates: A New Method using Mathematical Morphology for the Segmentation of Structures Including DEHSI == ==== Publication ==== The rela...")
 
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Bibtex entry:
 
Bibtex entry:
<nowiki>
 
   
  +
<pre>
 
@Article{ xu.18.media,
 
@Article{ xu.18.media,
 
author = {Yongchao Xu and Baptiste Morel and Sonia Dahdouh and
 
author = {Yongchao Xu and Baptiste Morel and Sonia Dahdouh and
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year = 2018,
 
year = 2018,
 
pages = {1--23},
 
pages = {1--23},
 
url = {http://publications.lrde.epita.fr/xu.18.media},
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.},
 
url = {http://publications.lrde.epita.fr/xu.18.media.pdf},
 
 
note = {To appear}
 
note = {To appear}
 
}
 
}
</nowiki>
+
</pre>
   
   
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==== Copyright Notice ====
 
==== Copyright Notice ====
   
[http://www.lrde.epita.fr LRDE] is the copyright holder of all the codes included in the archive.
+
The copyright holders of the main code included in the archive are:
  +
* [http://www.lrde.epita.fr LRDE]
  +
* FIXME
  +
* FIXME
   
 
You are allowed to use these codes and software for research purpose. If so, please specify the following copyright: "Copyright (c) 2018. EPITA Research and Development Laboratory (LRDE)". You are '''not''' allowed to redistribute these codes.
 
You are allowed to use these codes and software for research purpose. If so, please specify the following copyright: "Copyright (c) 2018. EPITA Research and Development Laboratory (LRDE)". You are '''not''' allowed to redistribute these codes.

Revision as of 10:24, 3 May 2018

The Challenge of Cerebral MRI in Neonates: A New Method using Mathematical Morphology for the Segmentation of Structures Including DEHSI

Publication

The related publication is available from this page.

Bibtex entry:

@Article{         xu.18.media,
  author        = {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},
  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},
  year          = 2018,
  pages         = {1--23},
  url =	 {http://publications.lrde.epita.fr/xu.18.media},
  note = {To appear}
}


Software

Available soon...

If this software is used in the context of a scientific publication, please cite the paper mentioned above.

Copyright Notice

The copyright holders of the main code included in the archive are:

You are allowed to use these codes and software for research purpose. If so, please specify the following copyright: "Copyright (c) 2018. EPITA Research and Development Laboratory (LRDE)". You are not allowed to redistribute these codes. You are not allowed to redistribute the dehsi.nii example.