Linguraru Marius George, Pura John A, Chowdhury Ananda S, Summers Ronald M
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Med Image Comput Comput Assist Interv. 2010;13(Pt 3):89-96. doi: 10.1007/978-3-642-15711-0_12.
The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis (CAD) applications. Diagnosis also relies on the comprehensive analysis of multiple organs and quantitative measures of soft tissue. An automated method optimized for medical image data is presented for the simultaneous segmentation of four abdominal organs from 4D CT data using graph cuts. Contrast-enhanced CT scans were obtained at two phases: non-contrast and portal venous. Intra-patient data were spatially normalized by non-linear registration. Then 4D erosion using population historic information of contrast-enhanced liver, spleen, and kidneys was applied to multi-phase data to initialize the 4D graph and adapt to patient specific data. CT enhancement information and constraints on shape, from Parzen windows, and location, from a probabilistic atlas, were input into a new formulation of a 4D graph. Comparative results demonstrate the effects of appearance and enhancement, and shape and location on organ segmentation.
医学图像的解读受益于解剖学和生理学先验知识，以优化计算机辅助诊断（CAD）应用。诊断还依赖于对多个器官的综合分析以及软组织的定量测量。本文提出了一种针对医学图像数据优化的自动化方法，用于使用图割从4D CT数据中同时分割四个腹部器官。在两个阶段获取对比增强CT扫描：非增强期和门静脉期。通过非线性配准对患者体内数据进行空间归一化。然后，利用对比增强肝脏、脾脏和肾脏的群体历史信息对多期数据应用4D腐蚀，以初始化4D图并适应患者特定数据。将CT增强信息以及来自Parzen窗的形状约束和来自概率图谱的位置约束输入到4D图的新公式中。比较结果展示了外观和增强、形状和位置对器官分割的影响。
