Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK.
Department of Information Engineering, University of Brescia, Brescia, Italy.
Hum Brain Mapp. 2021 Dec 1;42(17):5563-5580. doi: 10.1002/hbm.25636. Epub 2021 Oct 1.
Ultra-high-field magnetic resonance imaging (MRI) enables sub-millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso-scale. An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures. Despite recent efforts in brain imaging analysis, the literature lacks in accurate and fast methods for segmenting 7-tesla (7T) brain MRI. We here present CEREBRUM-7T, an optimised end-to-end convolutional neural network, which allows fully automatic segmentation of a whole 7T T1 MRI brain volume at once, without partitioning the volume, pre-processing, nor aligning it to an atlas. The trained model is able to produce accurate multi-structure segmentation masks on six different classes plus background in only a few seconds. The experimental part, a combination of objective numerical evaluations and subjective analysis, confirms that the proposed solution outperforms the training labels it was trained on and is suitable for neuroimaging studies, such as layer functional MRI studies. Taking advantage of a fine-tuning operation on a reduced set of volumes, we also show how it is possible to effectively apply CEREBRUM-7T to different sites data. Furthermore, we release the code, 7T data, and other materials, including the training labels and the Turing test.
超高场磁共振成像（MRI）能够实现亚毫米分辨率的人脑成像，允许在介观尺度上研究皮质层的功能回路。许多功能和结构神经影像学研究的一个基本步骤是分割，即将 MR 图像分割为解剖结构的操作。尽管在脑成像分析方面最近做了很多努力，但文献中缺乏准确和快速的方法来分割 7 特斯拉（7T）脑 MRI。我们在这里提出 CEREBRUM-7T，这是一种经过优化的端到端卷积神经网络，它允许一次全自动分割整个 7T T1 磁共振脑容积，无需分割体积、预处理或将其与图谱对齐。经过训练的模型能够在短短几秒钟内生成准确的多结构分割掩模，涵盖六个不同类别加背景。实验部分包括客观数值评估和主观分析的组合，证实了所提出的解决方案优于其训练标签，适用于神经影像学研究，如层功能 MRI 研究。利用在少量体积上进行微调操作，我们还展示了如何有效地将 CEREBRUM-7T 应用于不同站点的数据。此外，我们还发布了代码、7T 数据和其他材料，包括训练标签和图灵测试。
