Fast Brain Volumetric Segmentation from T1 MRI Scans

  • Ananya AnandEmail author
  • Namrata AnandEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)


In this paper, we train a state-of-the-art deep neural network segmentation model to do fast brain volumetric segmentation from T1 MRI scans. We use image data from the ADNI and OASIS image collections and corresponding FreeSurfer automated segmentations to train our segmentation model. The model is able to do whole brain segmentation across 13 anatomical classes in seconds; in contrast, FreeSurfer takes several hours per volume. We show that this trained model can be used as a prior for other segmentation tasks, and that pre-training the model in this manner leads to better brain structure segmentation performance on a small dataset of expert-given manual segmentations.


Magnetic resonance imaging Supervised machine learning Neural networks (computer) Artificial intelligence Computer vision systems 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Warren Alpert School of MedicineBrown UniversityProvidenceUSA
  2. 2.Bioengineering DepartmentStanford UniversityStanfordUSA

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