Institute for Medical Image Computing, Fraunhofer MEVIS, Bremen, Germany.
Int J Numer Method Biomed Eng. 2013 Sep;29(9):938-63. doi: 10.1002/cnm.2582. Epub 2013 Aug 14.
Although computer assistance has become common in medical practice, some of the most challenging tasks that remain unsolved are in the area of automatic detection and recognition. The human visual perception is in general far superior to computer vision algorithms. Object-based image analysis is a relatively new approach that aims to lift image analysis from a pixel-based processing to a semantic region-based processing of images. It allows effective integration of reasoning processes and contextual concepts into the recognition method. In this paper, we present an approach that applies object-based image analysis to the task of detecting the spine in computed tomography images. A spine detection would be of great benefit in several contexts, from the automatic labeling of vertebrae to the assessment of spinal pathologies. We show with our approach how region-based features, contextual information and domain knowledge, especially concerning the typical shape and structure of the spine and its components, can be used effectively in the analysis process. The results of our approach are promising with a detection rate for vertebral bodies of 96% and a precision of 99%. We also gain a good two-dimensional segmentation of the spine along the more central slices and a coarse three-dimensional segmentation.
尽管计算机辅助已经在医学实践中变得很常见，但一些仍然未解决的最具挑战性的任务是在自动检测和识别领域。人类的视觉感知通常远远优于计算机视觉算法。基于对象的图像分析是一种相对较新的方法，旨在将图像分析从基于像素的处理提升到基于语义区域的处理。它允许将推理过程和上下文概念有效地集成到识别方法中。在本文中，我们提出了一种应用基于对象的图像分析来检测计算机断层扫描图像中脊柱的方法。在许多情况下，脊柱检测将非常有益，从自动标记椎体到评估脊柱病变。我们通过我们的方法展示了如何在分析过程中有效地使用基于区域的特征、上下文信息和领域知识，特别是有关脊柱及其组成部分的典型形状和结构的知识。我们的方法取得了很好的结果，椎体的检测率为 96%，精度为 99%。我们还沿着更中心的切片对脊柱进行了良好的二维分割，并进行了粗略的三维分割。
