Difference between revisions of "Publications/gasnault.21.seminar"
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| year = 2021 |
| year = 2021 |
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| number = 2119 |
| number = 2119 |
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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. |
+ | | 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 |
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| id = gasnault.21.seminar |
| id = gasnault.21.seminar |
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| − | | lrdepaper = https://www.lrde.epita.fr/dload/202106-Seminar/2119.pdf |
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| − | |||
| − | == Source code == |
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| − | |||
| − | Source code for this project can be found [https://gitlab.lrde.epita.fr/lgasnault/neobrainseg here]. |
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Latest revision as of 15:45, 10 March 2022
- Authors
- Louis Gasnault
- Type
- techreport
- Year
- 2021
- Number
- 2119
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.