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 enabling high precision segmentation with minimal compute. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods. Here, we used a unique dataset comprising 568 T1-weighted (T1w) MR images from 191 different studies in combination with cutting edge deep learning methods to build a fast, high-precision brain extraction tool called deepbet. deepbet uses LinkNet, a modern UNet architecture, in a two stage prediction process. This increases its segmentation performance, setting a novel state-of-the-art performance during cross-validation with a median Dice score (DSC) of 99.0% on unseen datasets, outperforming current state of the art models (DSC = 97.8% and DSC = 97.9%). While current methods are more sensitive to outliers, resulting in Dice scores as low as 76.5%, deepbet manages to achieve a Dice score of > 96.9% for all samples. Finally, our model accelerates brain extraction by a factor of ~10 compared to current methods, enabling the processing of one image in ~2 seconds on low level hardware.

翻译：磁共振成像（MRI）数据中的脑提取是许多神经影像预处理流程中的关键分割步骤。图像分割是近年来深度学习影响最为显著的研究领域之一，能够以极低计算量实现高精度分割。因此，传统脑提取方法正逐步被基于深度学习的方法所取代。本研究采用包含来自191项不同研究的568张T1加权（T1w）MR图像组成的独特数据集，结合前沿深度学习方法，构建了一种名为deepbet的快速高精度脑提取工具。deepbet采用现代UNet架构LinkNet，通过两阶段预测流程提升分割性能，在交叉验证中创下最新最优性能——在未见数据集上获得99.0%的中位Dice系数（DSC），显著优于当前最优模型（DSC分别为97.8%和97.9%）。当前方法对异常值更敏感（Dice评分可低至76.5%），而deepbet对所有样本的Dice评分均能保持在96.9%以上。最终，我们的模型将脑提取速度提升约10倍，在低配置硬件上处理单张图像仅需约2秒。