deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks

Fisch L.; Zumdick S.; Barkhau C.; Emden D.; Ernsting J.; Leenings R.; Sarink K.; Winter N.R.; Risse B.; Dannlowski U.; Hahn T.

Research article (journal) | Peer reviewed

Abstract

Background: Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods. Method: Here, we used a unique dataset compilation comprising 7837 T1-weighted (T1w) MR images from 191 different OpenNeuro datasets in combination with advanced deep learning methods to build a fast, high-precision brain extraction tool called deepbet. Results: deepbet sets a novel state-of-the-art performance during cross-dataset validation with a median Dice score (DSC) of 99.0 on unseen datasets, outperforming the current best performing deep learning (DSC=97.9) and classic (DSC=96.5) methods. While current methods are more sensitive to outliers, deepbet achieves a Dice score of >97.4 across all 7837 images from 191 different datasets. This robustness was additionally tested in 5 external datasets, which included challenging clinical MR images. During visual exploration of each method's output which resulted in the lowest Dice score, major errors could be found for all of the tested tools except deepbet. Finally, deepbet uses a compute efficient variant of the UNet architecture, which accelerates brain extraction by a factor of ≈10 compared to current methods, enabling the processing of one image in ≈2 s on low level hardware. Conclusions: In conclusion, deepbet demonstrates superior performance and reliability in brain extraction across a wide range of T1w MR images of adults, outperforming existing top tools. Its high minimal Dice score and minimal objective errors, even in challenging conditions, validate deepbet as a highly dependable tool for accurate brain extraction. deepbet can be conveniently installed via “pip install deepbet” and is publicly accessible at https://github.com/wwu-mmll/deepbet.

Details about the publication

JournalComputers in Biology and Medicine
Volume179
StatusPublished
Release year2024
Language in which the publication is writtenEnglish
DOI10.1016/j.compbiomed.2024.108845
Link to the full texthttps://api.elsevier.com/content/abstract/scopus_id/85198234727
KeywordsBrain extraction; Deep learning; Neural network; Skull stripping

Authors from the University of Münster

Barkhau, Carlotta Bo Catharine
Institute of Translational Psychiatry
Dannlowski, Udo
Institute of Translational Psychiatry
Emden, Daniel
Institute of Translational Psychiatry
Ernsting, Jan
Institute for Geoinformatics (ifgi)
Fisch, Lukas
Center for Nonlinear Science
Hahn, Tim
Institute of Translational Psychiatry
Leenings, Ramona
Institute of Translational Psychiatry
Risse, Benjamin
Professorship of Geoinformatics for Sustainable Development (Prof. Risse)
Sarink, Kelvin
Institute of Translational Psychiatry
Winter, Nils
Institute of Translational Psychiatry
Zumdick, Stefan
Institute of Translational Psychiatry