QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Guha Roy, A; Conjeti, S; Navab, N; Wachinger, C; Alzheimer's Disease Neuroimaging Initiative,

Published Date

  • February 1, 2019

Published In

Volume / Issue

  • 186 /

Start / End Page

  • 713 - 727

PubMed ID

  • 30502445

Pubmed Central ID

  • 30502445

Electronic International Standard Serial Number (EISSN)

  • 1095-9572

Digital Object Identifier (DOI)

  • 10.1016/j.neuroimage.2018.11.042

Language

  • eng

Conference Location

  • United States