MRI segmentation of the human brain: challenges, methods, and applications.

MRI segmentation of the human brain: challenges, methods, and applications.
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DOI:
10.1155/2015/450341
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发表时间:
2015
影响因子:
--
通讯作者:
Philips W
Philips W
中科院分区:
工程技术4区
文献类型:
--
作者:
Despotović I;Goossens B;Philips W

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图像分割是医学图像分析中最重要的任务之一,在许多临床应用中往往是第一步也是最关键的一步。在脑MRI分析中,图像分割通常用于测量和可视化大脑的解剖结构,分析大脑的变化,划定病理区域,并用于手术规划和图像引导的干预。在过去的几十年里,各种不同精度和复杂程度的分割技术被开发出来并在文献中得到了报道。在本文中,我们回顾了最流行的脑MRI分割方法。我们将重点介绍它们之间的差异,并讨论它们的功能、优势和局限性。为了解决脑MRI分割问题的复杂性和挑战性,我们首先介绍了图像分割的基本概念。然后,我们解释了不同的MRI预处理步骤,包括图像配准、偏场校正和非脑组织的去除。最后,在回顾了不同的脑MRI分割方法之后,讨论了脑MRI分割中的有效性问题。
Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation.
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