Wassermann Demian, Makris Nikos, Rathi Yogesh, Shenton Martha, Kikinis Ron, Kubicki Marek, Westin Carl-Fredrik
Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Med Image Comput Comput Assist Interv. 2013;16(Pt 1):647-54. doi: 10.1007/978-3-642-40811-3_81.
The main contribution of this work is the careful syntactical definition of major white matter tracts in the human brain based on a neuroanatomist's expert knowledge. We present a technique to formally describe white matter tracts and to automatically extract them from diffusion MRI data. The framework is based on a novel query language with a near-to-English textual syntax. This query language allows us to construct a dictionary of anatomical definitions describing white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This enables automated coherent labeling of white matter anatomy across subjects. We use our method to encode anatomical knowledge in human white matter describing 10 association and 8 projection tracts per hemisphere and 7 commissural tracts. The technique is shown to be comparable in accuracy to manual labeling. We present results applying this framework to create a white matter atlas from 77 healthy subjects, and we use this atlas in a proof-of-concept study to detect tract changes specific to schizophrenia.
这项工作的主要贡献在于,基于神经解剖学家的专业知识,对人类大脑中的主要白质束进行了细致的句法定义。我们提出了一种技术,用于正式描述白质束并从扩散磁共振成像(MRI)数据中自动提取它们。该框架基于一种具有近乎英语文本语法的新型查询语言。这种查询语言使我们能够构建一个描述白质束的解剖学定义词典。这些定义包括相邻的灰质和白质区域以及空间关系规则。这使得能够对不同受试者的白质解剖结构进行自动连贯标注。我们使用我们的方法对人类白质中的解剖学知识进行编码,每个半球描述10条联合束和8条投射束以及7条连合束。结果表明,该技术在准确性上与手动标注相当。我们展示了将此框架应用于从77名健康受试者创建白质图谱的结果,并在一项概念验证研究中使用该图谱来检测精神分裂症特有的束变化。