Automated Brain Masking of Fetal Functional MRI with Open Data.

Autor: Rutherford S; Donders Institute, Radboud University Medical Center, Nijmegen, The Netherlands. saige.rutherford@donders.ru.nl.; Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA. saige.rutherford@donders.ru.nl., Sturmfels P; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA., Angstadt M; Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA., Hect J; Department of Psychology, Wayne State University, Detroit, MI, USA., Wiens J; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA., van den Heuvel MI; Department of Cognitive Neuropsychology, University of Tilburg, Tilburg, The Netherlands., Scheinost D; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.; Department of Statistics and Data Science, Yale University, New Haven, CT, USA.; Child Study Center, Yale School of Medicine, New Haven, CT, USA., Sripada C; Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA., Thomason M; Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA.; Department of Population Health, New York University School of Medicine, New York, NY, USA.
Jazyk: angličtina
Zdroj: Neuroinformatics [Neuroinformatics] 2022 Jan; Vol. 20 (1), pp. 173-185. Date of Electronic Publication: 2021 Jun 15.
DOI: 10.1007/s12021-021-09528-5
Abstrakt: Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.
(© 2021. The Author(s).)
Databáze: MEDLINE
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