Dadar Mahsa, Collins D Louis
NeuroImaging and Surgical Tools Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
Magn Reson Med. 2021 Apr;85(4):1881-1894. doi: 10.1002/mrm.28547. Epub 2020 Oct 11.
Tissue segmentation from T -weighted (T1W) MRI is a critical requirement in many neuroscience and clinical applications. However, accurate tissue segmentation is challenging because of the variabilities in tissue intensity profiles caused by differences in scanner models, acquisition protocols, and age. In addition, many methods assume healthy anatomy and fail in the presence of pathology such as white matter hyperintensities (WMHs). We present BISON (Brain tISsue segmentatiON), a new pipeline for tissue segmentation using a random forest classifier and a set of intensity and location priors based on T1W MRI.
BISON was developed and cross-validated using multiscanner manual labels of 72 subjects aged 5 to 96 years. We also assessed the test-retest reliability of BISON on two data sets: 20 subjects with scan/rescan MR images and manual segmentations and 90 scans from a single individual. The results were compared against Atropos, a state-of-the-art commonly used tissue classification method from advanced normalization tools (ANTs).
BISON cross-validation dice kappa values against manual segmentations of 72 MRI volumes yielded κ = 0.88, κ = 0.85, κ = 0.77, outperforming Atropos (κ = 0.79, κ = 0.84, κ = 0.64), test-retest values on 20 subjects of κ = 0.94, κ = 0.92, κ = 0.77 outperforming both manual (κ = 0.92, κ = 0.91, κ =0.74) and Atropos (κ = 0.87, κ = 0.92, κ = 0.79). Finally, BISON outperformed Atropos, FAST (fast automated segmentation tool) from the FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library, and SPM12 (statistical parametric mapping 12) in the presence of WMHs.
BISON can provide accurate and robust segmentations in data from various age ranges and scanner models, making it ideal for performing tissue classification in large multicenter and multiscanner databases.
从T加权（T1W）磁共振成像（MRI）中进行组织分割是许多神经科学和临床应用中的一项关键要求。然而，由于扫描仪型号、采集协议和年龄差异导致组织强度分布存在变异性，准确的组织分割具有挑战性。此外，许多方法假定解剖结构正常，在存在诸如白质高信号（WMH）等病变的情况下会失效。我们提出了BISON（脑组织分割），这是一种使用随机森林分类器和基于T1W MRI的一组强度和位置先验信息进行组织分割的新流程。
使用72名年龄在5至96岁受试者的多扫描仪手动标注数据开发并交叉验证了BISON。我们还在两个数据集上评估了BISON的重测信度：20名有扫描/重新扫描MR图像及手动分割的受试者，以及来自一名个体的90次扫描。将结果与Atropos进行比较，Atropos是高级归一化工具（ANTs）中一种常用的先进组织分类方法。
BISON针对72个MRI容积的手动分割的交叉验证骰子卡帕值产生κ = 0.88、κ = 0.85、κ = 0.77，优于Atropos（κ = 0.79、κ = 0.84、κ = 0.64）；20名受试者的重测值κ = 0.94、κ = 0.92、κ = 0.77，优于手动分割（κ = 0.92、κ = 0.91、κ = 0.74）和Atropos（κ = 0.87、κ = 0.92、κ = 0.79）。最后，在存在WMH的情况下，BISON优于Atropos、来自FMRIB（脑功能磁共振成像）软件库的FAST（快速自动分割工具）和SPM12（统计参数映射12）。
BISON能够在来自不同年龄范围和扫描仪型号的数据中提供准确且稳健的分割，使其非常适合在大型多中心和多扫描仪数据库中进行组织分类。
