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Fast Brain Volumetric Segmentation from T1 MRI Scans

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Advances in Computer Vision (CVC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 943))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Warren Alpert School of Medicine, Brown University, Providence, RI, USA

    Ananya Anand

  2. Bioengineering Department, Stanford University, Stanford, CA, USA

    Namrata Anand

Authors
  1. Ananya Anand
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  2. Namrata Anand
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Corresponding authors

Correspondence to Ananya Anand or Namrata Anand .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Anand, A., Anand, N. (2020). Fast Brain Volumetric Segmentation from T1 MRI Scans. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_30

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