Multi-atlas segmentation of biomedical images: A survey.
Multi-atlas segmentation of biomedical images: A survey.
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DOI：
10.1016/j.media.2015.06.012
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发表时间：
2015-08
影响因子：
10.9
通讯作者：
Sabuncu MR
中科院分区：
文献类型：
--
作者：
Iglesias JE;Sabuncu MR
多图谱分割(MAS)是由和的开创性工作首次引入并推广的，正在成为生物医学应用中最广泛和最成功的图像分割技术之一。通过操纵和利用整个数据集的“图谱”(先前已经被例如由专家手动标记的训练图像)，而不是一些基于模型的平均表示，MAS具有更好地捕捉解剖变化的灵活性，从而提供更好的分割精度。然而，这种好处通常是以较高的计算成本为代价的。计算机硬件和图像处理软件的最新进展有助于应对这一挑战，并促进了MAS的广泛采用。今天，MAS已经走过了很长的路，这种方法包括广泛的复杂算法，这些算法采用了机器学习、概率建模、优化和计算机视觉等领域的思想。本文综述了已发表的MAS算法以及将这些方法应用于各种生物医学问题的研究。在撰写这份调查时，我们有三个不同的目标。我们的主要目标是记录MAS最初是如何构思的，后来是如何发展的，现在是如何与替代方法相关的。其次，本文件旨在详细参考过去在MAS开展的研究活动，这些活动现在跨越了十多年(2003-2014年)，并产生了新的方法发展和具体应用的解决方案。最后，我们的目标也是对MAS的未来进行展望，我们相信MAS将是生物医学图像分割的主要方法之一。
Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of, and, is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of “atlases” (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 – 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.