# Knowing what you know in brain segmentation using Bayesian deep neural networks

## TL;DR

This paper introduces a Bayesian deep neural network that rapidly predicts brain segmentations from MRI scans, providing reliable uncertainty estimates that correlate with segmentation accuracy and quality control assessments.

## Contribution

The paper presents a novel spike-and-slab dropout variational inference method for Bayesian DNNs, improving segmentation accuracy and uncertainty quantification in brain MRI analysis.

## Key findings

- Outperforms previous methods in segmentation accuracy.
- Uncertainty estimates predict errors and quality control ratings.
- Works efficiently on large, multi-site datasets.

## Abstract

In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out dataset (n = 418). The network was trained using a novel spike-and-slab dropout-based variational inference approach. We show that, on these datasets, the proposed Bayesian DNN outperforms previously proposed methods, in terms of the similarity between the segmentation predictions and the FreeSurfer labels, and the usefulness of the estimate uncertainty of these predictions. In particular, we demonstrated that the prediction uncertainty of this network at each voxel is a good indicator of whether the network has made an error and that the uncertainty across the whole brain can predict the manual quality control ratings of a scan. The proposed Bayesian DNN method should be applicable to any new network architecture for addressing the segmentation problem.