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A deformable model for the reconstruction of the neonatal cortex

Research output: Chapter in Book/Report/Conference proceedingConference paper

Andreas Schuh, Antonios Makropoulos, Robert Wright, Emma C. Robinson, Nora Tusor, Johannes Steinweg, Emer Hughes, Lucilio Cordero Grande, Anthony Price, Jana Hutter, Joseph V. Hajnal, Daniel Rueckert

Original languageEnglish
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Pages800-803
Number of pages4
ISBN (Electronic)9781509011711
DOIs
Publication statusE-pub ahead of print - 19 Jun 2017
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: 18 Apr 201721 Apr 2017

Conference

Conference14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
CountryAustralia
CityMelbourne
Period18/04/201721/04/2017

Documents

  • A deformable model for_SCHUH_Publishedonline19June2017_GREEN AAM

    ISBI17_0391_FI.pdf, 1 MB, application/pdf

    26/07/2017

    Accepted author manuscript

    “© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted.

King's Authors

Abstract

We present a method based on deformable meshes for the reconstruction of the cortical surfaces of the developing human brain at the neonatal period. It employs a brain segmentation for the reconstruction of an initial inner cortical surface mesh. Errors in the segmentation resulting from poor tissue contrast in neonatal MRI and partial volume effects are subsequently accounted for by a local edge-based refinement. We show that the obtained surface models define the cortical boundaries more accurately than the segmentation. The surface meshes are further guaranteed to not intersect and subdivide the brain volume into disjoint regions. The proposed method generates topologically correct surfaces which facilitate both a flattening and spherical mapping of the cortex.

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