Shape-driven three-dimensional watersnake segmentation of biological membranes in electron tomography
Shape-driven three-dimensional watersnake segmentation of biological membranes in electron tomography
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DOI：
10.1109/tmi.2007.912390
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发表时间：
2008-05-01
影响因子：
10.6
通讯作者：
Ji, Qiang
中科院分区：
文献类型：
--
作者：
Nguyen, Hieu;Ji, Qiang
由于膜形态的显著复杂性和电子断层扫描体积中通常较差的图像质量，用于膜分割的当前自动方法表现不佳。用户必须求助于在体积的2-D切片上手动跟踪识别的图案，这种方法具有主观性并且非常劳动密集，从而防止了需要对许多体积进行比较分析的断层扫描数据的定量分析。为了克服这些局限性，我们开发了一种自动的3-D分割方法，充分利用先验知识的膜的形状以及由断层图像提供的3-D信息，并系统地结合这些知识与图像数据，以提高分割结果。该方法基于水蛇框架。通过将传统的分水岭分割转化为能量最小化问题，水蛇算法继承了分水岭方法的优点，同时克服了传统基于能量的分割方法的局限性。在我们以前的工作（H。Nguyen等人，2003），通过将平滑度并入分水岭分割成功地修改了原始水蛇模型。在这项工作中，我们进一步扩展该模型，将各种约束，代表我们的先验知识的全球形状的细胞功能分割的能量函数。分割，因此，可以通过最小化的能量函数服从形状先验约束。最后，数学框架被进一步扩展，从2-D到3-D，以便可以在3-D中进行分割，以利用由断层图像提供的附加信息。我们应用这种方法自动提取生物膜的不同复杂性，包括细菌壁和线粒体边界。
Due to the significant complexity of membrane morphology and the generally poor image quality in electron tomographic volumes, current automatic methods for segmentation of membranes perform poorly. Users must resort to manual tracing of recognized patterns on 2-D slices of the volume, a method that suffers from subjectivity and is very labor intensive, preventing quantitative analyses of tomographic data that require comparative analyses of many volumes. To overcome these limitations, we develop an automatic 3-D segmentation method that fully exploits the prior knowledge about the shape of the membranes as well as the 3-D information provided by the tomograms, and systematically combines this knowledge with the image data to improve segmentation results. The method is based on the watersnake framework. By mathematically reformulating the traditional watershed segmentation as an energy minimization problem, the watersnake inherits the many strengths of the watershed method while overcoming the limitations of the traditional energy-based segmentation methods. In our previous work (H. Nguyen et aL, 2003), the original watersnake model was successfully modified by incorporating smoothness into watershed segmentation. In this work, we further extend that model to incorporate into the energy function various constraints representing our prior knowledge about the global shape of the cellular features to be segmented. Segmentation can, therefore, be accomplished via minimization of the energy function subject to the shape prior constraints. Finally, the mathematical framework is further extended from 2-D to 3-D so that segmentation can be carried out in 3-D to take advantage of the additional information provided by the tomograms. We apply this method for the automatic extraction of biological membranes of varying complexities including those of bacterial walls and mitochondrial boundaries.