基于人工智能的白质纤维束分割方法与研究进展  

Methods and Research Progress of AI-Based White Matter Tract Segmentation

作　　者：宗芳荣 张迪[1] 魏婧怡 王怡然 刘勇[1] ZONG Fangrong;ZHANG Di;WEI Jingyi;WANG Yiran;LIIU Yong(School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing 100876,China;International School,Beijing University of Posts and Telecommunications,Beijing 100876,China;School of Computer Science,Beijing University of Posts and Telecommunications,Beijing 100876,China)

机构地区：[1]北京邮电大学人工智能学院,北京100876 [2]北京邮电大学国际学院,北京100876 [3]北京邮电大学计算机学院,北京100876

出　　处：《北京邮电大学学报》2023年第6期1-7,共7页Journal of Beijing University of Posts and Telecommunications

摘　　要：基于扩散磁共振成像数据进行纤维追踪可得到白质纤维轨迹,根据纤维特征将其分割成不同簇束有助于临床针对性分析和精确诊疗。首先介绍大脑白质纤维学和分割原理;然后归纳基于体素和基于纤维流线的方法,重点介绍不同基于人工智能的分割模型并设计实验以展示分割结果;最后总结纤维束分割方法面临的挑战,探讨该领域的研究趋势,展望人工智能技术应用的发展前景,为亚健康人和疾病患者的神经科学研究提供全面的纤维束分割方法总结与诊断支持。Diffusion magnetic resonance imaging allows mapping white matter fiber tracts via a process called tractography.Segmentation of white matter tracts according to fiber characteristics can assist statistical analysis and precision medicine.We introduce the principles of white matter tractography and segmentation firstly.Then we categorize state-of-the-art segmentation methods into voxel-based and fiberbased categories.Moreover,various artificial intelligence algorithms on segmentation are summarized and concluded,and an experiment is conducted to show the segmentation results.Finally,we discuss the challenges and research trends,and forecast the progress prospect of artificial intelligence in white matter tract segmentation.In summary,the review provides a comprehensive methodological summary and diagnostic support for downstream neuroscience research in sub-healthy individuals and patients.

关 键 词：扩散磁共振成像 白质纤维束 分割 人工智能 脑科学 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]