Lo Bethany P, Donnelly Miranda R, Barisano Giuseppe, Liew Sook-Lei
Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.
Department of Neurosurgery, Stanford University, Stanford, CA, United States.
Front Neuroimaging. 2023 Jan 10;1:1098604. doi: 10.3389/fnimg.2022.1098604. eCollection 2022.
Although automated methods for stroke lesion segmentation exist, many researchers still rely on manual segmentation as the gold standard. Our detailed, standardized protocol for stroke lesion tracing on high-resolution 3D T1-weighted (T1w) magnetic resonance imaging (MRI) has been used to trace over 1,300 stroke MRI. In the current study, we describe the protocol, including a step-by-step method utilized for training multiple individuals to trace lesions ("tracers") in a consistent manner and suggestions for distinguishing between lesioned and non-lesioned areas in stroke brains. Inter-rater and intra-rater reliability were calculated across six tracers trained using our protocol, resulting in an average intraclass correlation of 0.98 and 0.99, respectively, as well as a Dice similarity coefficient of 0.727 and 0.839, respectively. This protocol provides a standardized guideline for researchers performing manual lesion segmentation in stroke T1-weighted MRI, with detailed methods to promote reproducibility in stroke research.
尽管存在用于中风病灶分割的自动化方法,但许多研究人员仍将手动分割作为金标准。我们在高分辨率三维T1加权(T1w)磁共振成像(MRI)上进行中风病灶追踪的详细、标准化方案已被用于追踪1300多例中风MRI。在当前研究中,我们描述了该方案,包括用于训练多个个体以一致方式追踪病灶(“追踪者”)的逐步方法,以及区分中风大脑中病灶区域和非病灶区域的建议。对使用我们的方案训练的六名追踪者计算了评分者间和评分者内信度,平均组内相关系数分别为0.98和0.99,以及Dice相似系数分别为0.727和0.839。该方案为在中风T1加权MRI中进行手动病灶分割的研究人员提供了标准化指南,并提供了详细方法以促进中风研究的可重复性。