# Automated brain extraction of multi-sequence MRI using artificial neural networks

## TL;DR

This paper presents HD-BET, a neural network-based algorithm for brain extraction in MRI scans, which outperforms existing methods especially in pathological and heterogeneous datasets, enhancing robustness and accuracy.

## Contribution

Introduction of HD-BET, a novel neural network algorithm that improves brain extraction accuracy and robustness across diverse MRI datasets and pathologies.

## Key findings

- HD-BET outperforms six popular algorithms in large-scale datasets.
- Median improvements of +1.16 to +2.11 in DICE coefficient.
- Robust performance with pathological and varied MRI data.

## Abstract

Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aims to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.11 points for the DICE coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility our HD-BET prediction algorithm is made freely available (http://www.neuroAI-HD.org) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data.

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Source: https://tomesphere.com/paper/1901.11341