XSEDE-enabled high-throughput lesion activity assessment
XSEDE-enabled high-throughput lesion activity assessment
复制标题
支持 XSEDE 的高通量病变活动评估
DOI：
--
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
M. Ando
中科院分区：
文献类型：
--
作者：
Hui Zhang;M. Boyles;Guangchen Ruan;Huian Li;Hongwei Shen;M. Ando
龋齿病变活动性评估一直是龋齿治疗的常规诊断程序，传统上采用结合视觉和触觉检查的主观测量。最近，2D/3D图像处理和分析方法以及微聚焦x射线计算机断层扫描（μ-CT）硬件的进步，以及高性能计算能力的增强，创造了协同效应，正在彻底改变牙科计算的许多领域。在本文中，我们报告了这样一个基于xsede的高通量病变活动评估工作流，该工作流利用了2D/3D图像处理、视觉分析和高性能计算技术。我们的论文首先简要介绍了我们牙科研究中的图像数据集。然后，我们继续进行一系列二维图像分析，ROI分割和三维几何构造方法。通过结合牙科成像技术和2D/3D图像处理算法，我们将病变活动评估任务转化为计算机生成的病变模型的3D时间序列分析。在计算算法和实现模型的基础上，我们开发了一个高通量牙科计算工作流，利用MapReduce任务并行化牙科CT扫描的图像分析、感兴趣区域（ROI）的分割和病变体积的三维构建。我们展示了3d时间序列分析和其他几种信息表示的应用，这些信息表示应用于我们的病灶活动评估场景，重点是大规模牙科图像数据。
Caries lesion activity assessment has been a routine diagnostic procedure in dental caries management, traditionally employing subjective measurements incorporating visual and tactile inspections. Recently, advances in 2D/3D image processing and analysis methods and microfocus x-ray computerized tomography (μ-CT) hardware, together with increased power of high performance computing, have created a synergic effect that is revolutionizing many fields in dental computing. In this paper, we report such an XSEDE-enabled high-throughput lesion activity assessment workflow that exploits 2D/3D image processing, visual analytics, and high performance computing technologies. Our paper starts with a brief introduction of the image dataset in our dental studies. We then proceed to a family of 2D image analysis, ROI segmentation, and 3D geometric construction methods. By combining dental imaging technology and 2D/3D image processing algorithms, we transform the task of lesion activity assessment into a 3D-time series analysis of computer generated lesion models. Building on the computational algorithms and implementation models, we develop a high-throughput dental computing workflow exploiting MapReduce tasks to parallelize the image analysis of dental CT scans, the segmentation of region-of-interest (ROI), and the 3D construction of lesion volumes. We showcase the employment of 3D-time series analysis and several other information representations that are applied to our lesion activity assessment scenario focusing on large scale dental image data.