Computational Neuroimaging Lab, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962, NY, USA.
Center for the Developing Brain, Child Mind Institute, 445 Park Ave, New York, 10022, NY, USA.
Gigascience. 2016 Oct 25;5(1):45. doi: 10.1186/s13742-016-0150-5.
Skull-stripping is the procedure of removing non-brain tissue from anatomical MRI data. This procedure can be useful for calculating brain volume and for improving the quality of other image processing steps. Developing new skull-stripping algorithms and evaluating their performance requires gold standard data from a variety of different scanners and acquisition methods. We complement existing repositories with manually corrected brain masks for 125 T1-weighted anatomical scans from the Nathan Kline Institute Enhanced Rockland Sample Neurofeedback Study.
Skull-stripped images were obtained using a semi-automated procedure that involved skull-stripping the data using the brain extraction based on nonlocal segmentation technique (BEaST) software, and manually correcting the worst results. Corrected brain masks were added into the BEaST library and the procedure was repeated until acceptable brain masks were available for all images. In total, 85 of the skull-stripped images were hand-edited and 40 were deemed to not need editing. The results are brain masks for the 125 images along with a BEaST library for automatically skull-stripping other data.
Skull-stripped anatomical images from the Neurofeedback sample are available for download from the Preprocessed Connectomes Project. The resulting brain masks can be used by researchers to improve preprocessing of the Neurofeedback data, as training and testing data for developing new skull-stripping algorithms, and for evaluating the impact on other aspects of MRI preprocessing. We have illustrated the utility of these data as a reference for comparing various automatic methods and evaluated the performance of the newly created library on independent data.
颅骨剥离是从解剖学 MRI 数据中去除非脑组织的过程。该过程可用于计算脑容量，并提高其他图像处理步骤的质量。开发新的颅骨剥离算法并评估其性能需要来自各种不同扫描仪和采集方法的金标准数据。我们通过 Nathan Kline 研究所增强的 Rockland 样本神经反馈研究中的 125 个 T1 加权解剖扫描，用手动校正的脑掩模补充了现有的存储库。
颅骨剥离图像是使用半自动程序获得的，该程序使用基于非局部分割技术的脑提取（BEaST）软件对数据进行颅骨剥离，并手动纠正最差的结果。校正后的脑掩模被添加到 BEaST 库中，然后重复该过程，直到所有图像都有可接受的脑掩模为止。总共编辑了 85 个颅骨剥离图像，其中 40 个被认为不需要编辑。结果是 125 张图像的颅骨剥离脑掩模以及用于自动颅骨剥离其他数据的 BEaST 库。
从神经反馈样本中提取的颅骨剥离解剖图像可从预处理连接组项目中下载。研究人员可以使用这些脑掩模来改进神经反馈数据的预处理，作为开发新的颅骨剥离算法的训练和测试数据，并评估其对 MRI 预处理其他方面的影响。我们已经说明了这些数据的有用性，作为比较各种自动方法的参考，并评估了新创建的库在独立数据上的性能。
