Difference between revisions of "Publications/gasnault.21.seminar"

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| year = 2021
 
| year = 2021
 
| number = 2119
 
| number = 2119
| abstract = Brain development can be evaluate using brain Magnetic Resonance Imaging (MRI). It is useful when a preterm birth occur to ensure no brain diseases develops during postnatal period. Such diseases can be visible on T2-weighted MR image as high signal intensity (DEHSI). To assess the presence of white matter hyperintensities, we will work on a new implementation of a robust, semi-automated framework, based on mathematical morphology, specialized on neonate brain segmentation. We will go though related works, the implementation of the different steps and the difficulties we encountered. By the end we did not finish our version but it is in good shape for further development.
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| abstract = Brain development can be evaluated using brain Magnetic Resonance Imaging (MRI). It is useful in cases of preterm birth to ensure that no brain disease develops during the postnatal period. Such diseases can be visible on T2-weighted MR image as high signal intensity (DEHSI). To assess the presence of white matter hyperintensities, this work implements a new robust, semi-automated frameworkbased on mathematical morphology, specialized on neonate brain segmentation. We will go over the related work, the implementation of the different steps and the difficulties encountered. In the end, the version developped during this internship is not completely finished but it is in good shape for a later finalization.
 
| type = techreport
 
| type = techreport
 
| id = gasnault.21.seminar
 
| id = gasnault.21.seminar

Latest revision as of 15:45, 10 March 2022

Abstract

Brain development can be evaluated using brain Magnetic Resonance Imaging (MRI). It is useful in cases of preterm birth to ensure that no brain disease develops during the postnatal period. Such diseases can be visible on T2-weighted MR image as high signal intensity (DEHSI). To assess the presence of white matter hyperintensities, this work implements a new robust, semi-automated frameworkbased on mathematical morphology, specialized on neonate brain segmentation. We will go over the related work, the implementation of the different steps and the difficulties encountered. In the end, the version developped during this internship is not completely finished but it is in good shape for a later finalization.