Automated Brain Masking of Fetal Functional MRI with Open Data

• DOI: 10.1007/s12021-021-09528-5
• Publication Date: 2021-06-15T11:02:39Z
• Authors: Saige Rutherford; Pascal Sturmfels; Mike Angstadt; Jasmine Hect; Jenna Wiens; Marion I. van den...; Dustin Scheinost; Chandra Sripada; Moriah Thomason
• Keywords: 0301 basic medicine; CONSTRUCTION; fMRI; Brain; Convolutional neural network; Deep learning; ATLAS; Magnetic Resonance Imaging; Open-source software; Fetal; NETWORKS; 03 medical and health sciences; Fetus; CONNECTIVITY; REGISTRATION; Image Processing, Computer-Assisted; Original Article; Neural Networks, Computer; Functional imaging; Brain segmentation

AbstractFetal 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.