FastSurfer - A fast and accurate deep learning based neuroimaging pipeline
| Autor: | Bruce Fischl, Leonie Henschel, Kersten Diers, Santiago Estrada, Martin Reuter, Sailesh Conjeti |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Artificial intelligence Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 0302 clinical medicine methods [Magnetic Resonance Imaging] methods [Image Processing Computer-Assisted] Image Processing Computer-Assisted Segmentation 05 social sciences Image and Video Processing (eess.IV) Brain Human brain Magnetic Resonance Imaging medicine.anatomical_structure Neurology Embedding Neurons and Cognition (q-bio.NC) methods [Neuroimaging] Surface reconstruction Freesurfer Cognitive Neuroscience Reliability (computer networking) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Neuroimaging 050105 experimental psychology Article lcsh:RC321-571 03 medical and health sciences Deep Learning medicine FOS: Electrical engineering electronic engineering information engineering Dementia Humans 0501 psychology and cognitive sciences Cortical surface ddc:610 diagnostic imaging [Brain] lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Computational neuroimaging business.industry Deep learning Reproducibility of Results Pattern recognition Electrical Engineering and Systems Science - Image and Video Processing medicine.disease Pipeline (software) Structural MRI Quantitative Biology - Neurons and Cognition FOS: Biological sciences business 030217 neurology & neurosurgery Software |
| Zdroj: | NeuroImage, Vol 219, Iss, Pp 117012-(2020) NeuroImage NeuroImage 219, 117012-(2020). doi:10.1016/j.neuroimage.2020.117012 |
| ISSN: | 1095-9572 |
| Popis: | Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation. To this end, we introduce an advanced deep learning architecture capable of whole brain segmentation into 95 classes. The network architecture incorporates local and global competition via competitive dense blocks and competitive skip pathways, as well as multi-slice information aggregation that specifically tailor network performance towards accurate segmentation of both cortical and sub-cortical structures. Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. This approach provides a full FreeSurfer alternative for volumetric analysis (in under 1 minute) and surface-based thickness analysis (within only around 1h runtime). For sustainability of this approach we perform extensive validation: we assert high segmentation accuracy on several unseen datasets, measure generalizability and demonstrate increased test-retest reliability, and high sensitivity to group differences in dementia. Comment: Submitted to NeuroImage |
| Databáze: | OpenAIRE |
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