Lam Irma, Cunningham Michael, Birgfeld Craig, Speltz Matthew, Shapiro Linda
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:758-61. doi: 10.1109/EMBC.2014.6943701.
Craniosynostosis, a disorder in which one or more fibrous joints of the skull fuse prematurely, causes skull malformation and may be associated with increased intracranial pressure and developmental delays. In order to perform medical research studies that relate phenotypic abnormalities to outcomes such as cognitive ability or results of surgery, biomedical researchers need an automated methodology for quantifying the degree of abnormality of the disorder. This paper addresses that need by proposing a set of features derived from CT scans of the skull that can be used for this purpose. A thorough set of experiments is used to evaluate the features as compared to two human craniofacial experts in a ranking evaluation.
颅缝早闭是一种颅骨的一个或多个纤维关节过早融合的病症,会导致颅骨畸形,并可能与颅内压升高和发育迟缓有关。为了开展将表型异常与认知能力或手术结果等预后相关联的医学研究,生物医学研究人员需要一种用于量化该病症异常程度的自动化方法。本文通过提出一组可从颅骨CT扫描中得出的、能用于此目的的特征,满足了这一需求。在一项排序评估中,通过一系列全面的实验,将这些特征与两位人类颅面专家的评估结果进行比较,以对这些特征进行评估。