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Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks.

Patrick McClure1,2, Nao Rho1,2, John A Lee2,3

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Frontiers in Neuroinformatics
|November 22, 2019
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This study introduces a Bayesian deep neural network (DNN) for rapid MRI segmentation, significantly outperforming existing methods. The network

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Bayesian neural networksautomated quality controlbrain segmentationdeep learningmagnetic resonance imagingvariational inference

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Image Analysis

Background:

  • Accurate segmentation of structural MRI volumes is crucial for neurological research.
  • Traditional segmentation methods, like FreeSurfer, can be time-consuming.
  • Deep learning models offer potential for faster segmentation but require robust uncertainty estimation.

Purpose of the Study:

  • To develop and evaluate a Bayesian deep neural network (DNN) for accelerated FreeSurfer segmentation of structural MRI.
  • To assess the performance and uncertainty estimation capabilities of the proposed Bayesian DNN compared to existing methods.

Main Methods:

  • A novel Bayesian DNN utilizing a spike-and-slab dropout-based variational inference approach was developed.
  • The network was trained and validated on a large, multi-site dataset (n=11,480) and a held-out dataset (n=418).
  • Performance was evaluated based on segmentation accuracy and the utility of prediction uncertainty.

Main Results:

  • The Bayesian DNN achieved faster segmentation (minutes vs. hours) while maintaining high accuracy.
  • It outperformed previous methods in segmentation prediction similarity to FreeSurfer labels.
  • Voxel-wise prediction uncertainty effectively indicated segmentation errors and correlated with manual quality control ratings.

Conclusions:

  • The proposed Bayesian DNN provides an efficient and accurate method for structural MRI segmentation.
  • Its uncertainty estimation is a valuable tool for assessing segmentation reliability and scan quality.
  • This approach is adaptable to various network architectures for segmentation tasks.