基于先验知识的几何变形模型脑结构多目标三维分割

Mohamed Baghdadi, Nacéra Benamrane, Mounir Boukadoum, Lakhdar Sais
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引用次数: 0

摘要

三维磁共振图像中的脑结构分割对于理解神经退行性疾病至关重要。手动分割容易出错,需要强大的自动化技术。在本文中,我们介绍了一种新的鲁棒方法,用于同时分割MRI图像中的多个大脑结构。我们的方法涉及三维表面向预定义解剖目标的并发演化,采用高效的多目标广义快速推进方法(MOGFMM)进行同时目标检测。此外,我们提出了一个有效的进化函数,该函数集成了解剖学和概率地图集的先验知识,以及分割结构之间的空间关系。每个可变形表面对应一个特定的结构。为了验证我们的方法,我们在真实脑图像数据集(IBSR)上进行了实验,并将结果与几种最先进的方法进行了比较。所得结果令人满意,证明了该方法的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-object 3D segmentation of brain structures using a geometric deformable model with a priori knowledge
Brain structure segmentation in 3D Magnetic Resonance Images is crucial for understanding neurodegenerative disorders. Manual segmentation is error-prone, necessitating robust automated techniques. In this paper, we introduce a novel and robust approach for the simultaneous segmentation of multiple brain structures in MRI images. Our method involves the concurrent evolution of 3D surfaces toward predefined anatomical targets, employing an efficient multi-object generalized fast marching method (MOGFMM) for simultaneous object detection. Additionally, we propose an effective evolution function that integrates prior knowledge from anatomical and probabilistic atlases, as well as spatial relationships among the segmented structures. Each deformable surface corresponds to a specific structure. To validate our approach, we conducted experiments on a dataset of real brain images (IBSR) and compared the results with several state-of-the-art methods. The obtained results were promising, demonstrating the effectiveness and superiority of our developed method.
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