Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland.
Phys Med Biol. 2013 Jul 7;58(13):R97-129. doi: 10.1088/0031-9155/58/13/R97. Epub 2013 Jun 6.
MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.
基于 MRI 的脑肿瘤研究医学图像分析近年来受到关注,因为需要高效、客观地评估大量数据。虽然应用自动化方法分析脑肿瘤图像的开创性方法可以追溯到近二十年前,但目前的方法正变得更加成熟,越来越接近常规临床应用。本综述旨在通过简要介绍脑肿瘤和脑肿瘤成像首先提供全面概述。然后,我们回顾了与肿瘤脑图像相关的分割、配准和建模的最新技术,重点是神经胶质瘤。分割的目的是勾勒出肿瘤,包括其亚区室和周围组织,而配准和建模的主要挑战是处理肿瘤引起的形态变化。讨论了不同方法的优缺点,重点是可应用于标准临床成像方案的方法。最后,对当前状态进行了批判性评估,并探讨了未来的发展和趋势,特别关注放射肿瘤评估指南的最新发展。