Article ; Online: Development of the next-generation functional neuro-cognitive imaging protocol - Part 1: A 3D sliding-window convolutional neural net for automated brain parcellation.
2024 Volume 286, Page(s) 120505
Abstract: Functional MRI has emerged as a powerful tool to assess the severity of Post-concussion syndrome (PCS) and to provide guidance for neuro-cognitive therapists during treatment. The next-generation functional neuro-cognitive imaging protocol (fNCI2) has ... ...
Abstract | Functional MRI has emerged as a powerful tool to assess the severity of Post-concussion syndrome (PCS) and to provide guidance for neuro-cognitive therapists during treatment. The next-generation functional neuro-cognitive imaging protocol (fNCI2) has been developed to provide this assessment. This paper covers the first step in the analysis process, the development of a rapidly re-trainable, machine-learning, brain parcellation tool. The use of a sufficiently deep U-Net architecture encompassing a small (39 × 39 × 39 voxel input, 27 × 27 × 27 voxel output) sliding window to sample the entirety of the 3D image allows for the prediction of the entire image using only a single trained network. A large number of training, validating, and testing windows are thus generated from the 101 manually-labeled Mindboggle images, and full-image prediction is provided via a voxel-vote method using overlapping windows. Our method produces parcellated images that are highly consistent with standard atlas-based methods in under 3 min on a modern GPU, and the single network architecture allows for rapid retraining (<36 hr) as needed. |
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MeSH term(s) | Humans ; Neural Networks, Computer ; Brain ; Imaging, Three-Dimensional/methods ; Magnetic Resonance Imaging/methods ; Cognition ; Image Processing, Computer-Assisted/methods |
Language | English |
Publishing date | 2024-01-14 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 1147767-2 |
ISSN | 1095-9572 ; 1053-8119 |
ISSN (online) | 1095-9572 |
ISSN | 1053-8119 |
DOI | 10.1016/j.neuroimage.2023.120505 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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