Loss Leandro A, Bebis George, Chang Hang, Auer Manfred, Sarkar Purbasha, Parvin Bahram
Life Sciences Division, Lawrence Berkeley Nat Lab.
Dept of Computer Science, University of Nevada, Reno.
ACM BCB. 2012 Oct;2012:170-177. doi: 10.1145/2382936.2382958.
Electron tomography is a promising technology for imaging ultrastructures at nanoscale resolutions. However, image and quantitative analyses are often hindered by high levels of noise, staining heterogeneity, and material damage either as a result of the electron beam or sample preparation. We have developed and built a framework that allows for automatic segmentation and quantification of filamentous objects in 3D electron tomography. Our approach consists of three steps: (i) local enhancement of filaments by Hessian filtering; (ii) detection and completion (e.g., gap filling) of filamentous structures through tensor voting; and (iii) delineation of the filamentous networks. Our approach allows for quantification of filamentous networks in terms of their compositional and morphological features. We first validate our approach using a set of specifically designed synthetic data. We then apply our segmentation framework to tomograms of plant cell walls that have undergone different chemical treatments for polysaccharide extraction. The subsequent compositional and morphological analyses of the plant cell walls reveal their organizational characteristics and the effects of the different chemical protocols on specific polysaccharides.
电子断层扫描是一种很有前景的技术，可用于在纳米级分辨率下对超微结构进行成像。然而，图像和定量分析常常受到高水平噪声、染色异质性以及电子束或样品制备导致的材料损伤的阻碍。我们开发并构建了一个框架，该框架能够在三维电子断层扫描中对丝状物体进行自动分割和定量分析。我们的方法包括三个步骤：（i）通过黑塞滤波对细丝进行局部增强；（ii）通过张量投票检测并完成（例如，填补间隙）丝状结构；（iii）描绘丝状网络。我们的方法能够根据丝状网络的组成和形态特征对其进行定量分析。我们首先使用一组专门设计的合成数据验证了我们的方法。然后，我们将分割框架应用于经过不同化学处理以提取多糖的植物细胞壁的断层扫描图像。随后对植物细胞壁进行的组成和形态分析揭示了它们的组织特征以及不同化学方案对特定多糖的影响。
