Your browser doesn't support javascript.
loading
Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex.
Zeng, Xiangrui; Puonti, Oula; Sayeed, Areej; Herisse, Rogeny; Mora, Jocelyn; Evancic, Kathryn; Varadarajan, Divya; Balbastre, Yael; Costantini, Irene; Scardigli, Marina; Ramazzotti, Josephine; DiMeo, Danila; Mazzamuto, Giacomo; Pesce, Luca; Brady, Niamh; Cheli, Franco; Pavone, Francesco Saverio; Hof, Patrick R; Frost, Robert; Augustinack, Jean; van der Kouwe, André; Iglesias, Juan Eugenio; Fischl, Bruce.
Afiliação
  • Zeng X; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
  • Puonti O; Harvard Medical School, Department of Radiology, Boston, MA, USA.
  • Sayeed A; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
  • Herisse R; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark.
  • Mora J; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
  • Evancic K; Harvard Medical School, Department of Radiology, Boston, MA, USA.
  • Varadarajan D; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
  • Balbastre Y; Harvard Medical School, Department of Radiology, Boston, MA, USA.
  • Costantini I; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
  • Scardigli M; Harvard Medical School, Department of Radiology, Boston, MA, USA.
  • Ramazzotti J; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
  • DiMeo D; Harvard Medical School, Department of Radiology, Boston, MA, USA.
  • Mazzamuto G; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
  • Pesce L; Harvard Medical School, Department of Radiology, Boston, MA, USA.
  • Brady N; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
  • Cheli F; Harvard Medical School, Department of Radiology, Boston, MA, USA.
  • Pavone FS; National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy.
  • Hof PR; European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy.
  • Frost R; Department of Biology, University of Florence, Italy.
  • Augustinack J; European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy.
  • van der Kouwe A; European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy.
  • Iglesias JE; European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy.
  • Fischl B; National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy.
bioRxiv ; 2023 Dec 08.
Article em En | MEDLINE | ID: mdl-38106176
ABSTRACT
Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Leveraging recent advancements in ultra-high resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 µm, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article