Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
Med Image Anal. 2012 May;16(4):904-14. doi: 10.1016/j.media.2012.02.001. Epub 2012 Feb 11.
The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis 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 convolution using population training information of contrast-enhanced liver, spleen and kidneys was applied to multiphase 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, enhancement, shape and location on organ segmentation. All four abdominal organs were segmented robustly and accurately with volume overlaps over 93.6% and average surface distances below 1.1mm.
医学图像的解读得益于解剖学和生理学先验知识，以优化计算机辅助诊断应用。诊断还依赖于对多个器官的综合分析和软组织的定量测量。本文提出了一种针对医学图像数据的自动方法，使用图割对 4D CT 数据进行同时分割四个腹部器官。在两个阶段采集了增强 CT 扫描：非对比和门静脉期。通过非线性配准对患者内数据进行空间归一化。然后使用增强肝脏、脾脏和肾脏的人群训练信息对多期数据进行 4D 卷积，以初始化 4D 图并适应患者特定数据。将 CT 增强信息和形状约束（来自 Parzen 窗口）以及位置约束（来自概率图谱）输入到 4D 图的新公式中。对比结果证明了外观、增强、形状和位置对器官分割的影响。四个腹部器官都被稳健而准确地分割，体积重叠超过 93.6%，平均表面距离低于 1.1mm。
