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

arXiv: https://arxiv.org/abs/1901.11341 · HTML: https://mlsanity.com/paper/1901.11341/

## Key facts

- Cited by 134 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 30 of 134)

- [ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset](https://mlsanity.com/paper/2206.06694/) (2022)
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- [Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI](https://mlsanity.com/paper/2303.03758/) (2023)
- [The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients](https://mlsanity.com/paper/2112.06979/) (2021)
- [Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations](https://mlsanity.com/paper/2303.15065/) (2023)
- [Shifts 2.0: Extending The Dataset of Real Distributional Shifts](https://mlsanity.com/paper/2206.15407/) (2022)
- [Diffusion Models for Contrast Harmonization of Magnetic Resonance Images](https://mlsanity.com/paper/2303.08189/) (2023)
- [Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs](https://mlsanity.com/paper/2312.04215/) (2023)
- [Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model](https://mlsanity.com/paper/2108.09451/) (2021)
- [Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021](https://mlsanity.com/paper/2208.07167/) (2022)
- [Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model](https://mlsanity.com/paper/2305.03901/) (2023)
- [OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection](https://mlsanity.com/paper/2503.16247/) (2025)
- [Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification](https://mlsanity.com/paper/2204.13591/) (2022)
- [CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2307.04513/) (2023)
- [Boosting Skull-Stripping Performance for Pediatric Brain Images](https://mlsanity.com/paper/2402.16634/) (2024)
- [A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge](https://mlsanity.com/paper/2403.19425/) (2024)
- [k-strip: A novel segmentation algorithm in k-space for the application of skull stripping](https://mlsanity.com/paper/2205.09706/) (2022)
- [Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks](https://mlsanity.com/paper/2308.07003/) (2023)
- [Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation](https://mlsanity.com/paper/2311.08931/) (2023)
- [Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound](https://mlsanity.com/paper/2405.09959/) (2024)
- [De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy](https://mlsanity.com/paper/2410.12402/) (2024)
- [ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset](https://mlsanity.com/paper/2408.11142/) (2024)
- [Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis](https://mlsanity.com/paper/2110.06803/) (2021)
- [Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging](https://mlsanity.com/paper/2106.00919/) (2021)
- [A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation](https://mlsanity.com/paper/2309.06075/) (2023)
- [IGUANe: a 3D generalizable CycleGAN for multicenter harmonization of brain MR images](https://mlsanity.com/paper/2402.03227/) (2024)
- [Semi-Supervised Diffusion Model for Brain Age Prediction](https://mlsanity.com/paper/2402.09137/) (2024)
- [ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?](https://mlsanity.com/paper/2408.10966/) (2024)
- [BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning](https://mlsanity.com/paper/2604.27277/) (2026)

## Abstract (arXiv)

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.