Darrault Fanny, Dannhoff Guillaume, Chauvel Maëlig, Delmaire Théo, Louchez Simon, Poupon Cyril, Uszynski Ivy, Destrieux Christophe, Maldonado Igor Lima, Andersson Frédéric
Université de Tours, INSERM, Imaging Brain & Neuropsychiatry iBraiN U1253, 37032, Tours, France.
Centre Hospitalier Régional Universitaire de Strasbourg, Strasbourg, France.
J Anat. 2025 May;246(5):819-828. doi: 10.1111/joa.14167. Epub 2024 Oct 28.
Manual segmentation is an essential tool in the researcher's technical arsenal. It is a frequent practice necessary for image analysis in many protocols, especially in neuroimaging and comparative brain anatomy. In the framework of emergence of studies focusing on alternative animal models, manual segmentation procedures play a critical role. Nevertheless, this critical task is often assigned to students, a process that, unfortunately, tends to be time-consuming and repetitive. Well-conducted and well-described segmentation procedures can potentially guide novice and even expert operators and enhance research works' internal and external validity, making it possible to harmonize studies and facilitate data sharing. Furthermore, recent advances in neuroimaging, such as ex vivo imaging or ultra-high-field MRI, enable new acquisition modalities and the identification of minute structures that are barely visible with typical approaches. In this context of increasingly detailed and multimodal brain studies, reflecting on methodology is relevant and necessary. Because it is crucial to implement good practices in manual segmentation per se but also in the description of the segmentation procedures in research papers, we propose a general roadmap for optimizing the technique, its process and the reporting of manual segmentation. For each of them, the relevant elements of the literature have been collected and cited. The article is accompanied by a checklist that the reader can use to verify that the critical steps are being followed.
手动分割是研究人员技术工具库中的一项重要工具。在许多方案中,尤其是在神经成像和比较脑解剖学中,它是图像分析所需的常见操作。在专注于替代动物模型的研究兴起的框架下,手动分割程序起着关键作用。然而,这项关键任务通常交给学生,不幸的是,这个过程往往既耗时又重复。执行良好且描述清晰的分割程序有可能指导新手甚至专家操作人员,并提高研究工作的内部和外部效度,从而使研究得以协调并促进数据共享。此外,神经成像的最新进展,如离体成像或超高场磁共振成像,带来了新的采集方式,并能识别用典型方法几乎看不见的微小结构。在这种脑研究日益详细和多模态的背景下,思考方法学是相关且必要的。由于在手动分割本身以及在研究论文中分割程序的描述方面实施良好做法都至关重要,我们提出了一个优化手动分割技术、其过程及报告的总体路线图。对于其中的每一项,都收集并引用了文献中的相关要素。本文还附有一份清单,读者可用来核实关键步骤是否得到遵循。