QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy
Year: 2018
Type: article
Abstract: Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological analyses, but is computationally intense and can therefore delay the availability of image markers after scan acquisition. We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a MRI brain scan in 20 s. To enable training of the complex network with millions of learnable parameters using limited annotated data, we propose to first pre-train on auxiliary labels created from existing segmentation software. Subsequently, the pre-trained model is fine-tuned on manual labels to rectify errors in auxiliary labels. With this learning strategy, we are able to use large neuroimaging repositories without manual annotations for training. In an extensive set of evaluations on eight datasets that cover a wide age range, pathology, and different scanners, we demonstrate that QuickNAT achieves superior segmentation accuracy and reliability in comparison to state-of-the-art methods, while being orders of magnitude faster. The speed up facilitates processing of large data repositories and supports translation of imaging biomarkers by making them available within seconds for fast clinical decision making.
Source: NeuroImage
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,540
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Alzheimer's Drug Discovery Foundation, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, F. Hoffmann-La Roche, Medpace, Meso Scale Diagnostics, Nvidia, Synarc, Alzheimer's Disease Neuroimaging Initiative, Takeda Pharmaceuticals U.S.A., BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Eisai, Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst, Servier, IXICO, National Institutes of Health, Genentech, University of California, San Diego, Canadian Institutes of Health Research, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering
Awards: G1554142629, G1593117279, G1650819308, G2100670906, G2152643862, G2189010064, G2340207221, G2409989180, G2423387114, G2522063444, G2835601961, G29951154, G3131374986, G3158472349, G3216574877, G3553156159, G3710456697, G3752451908, G3870774570, G3908963977, G4119534169, G43180419, G4475667458, G4501223628, G4800177743, G5207404921, G5209990037, G528601746, G5324093878, G5365730499, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", G5654639750, Alzheimer's Disease Neuroimaging Initiative, G5740349702, G5850682332, G613619302, G6173816828, G6210868510, G6371410416, G6412110892, G6447063303, G6719828067, G6792506723, G6899163935, G72415811, G7298697282, G764699806, G7799614282, G781725486, G8228018387, G8281378126, G839208290, G8636843673, G8852128998, G8895907669, G8897063270, G8957659042, G9000122335
OpenAlex ID: W2901434856