Asman Andrew J, Landmana Bennett A
Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235.
Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235 ; Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218.
Proc SPIE Int Soc Opt Eng. 2012 Feb 23;8314:83140Y-. doi: 10.1117/12.910794.
Labeling or segmentation of structures of interest in medical imaging plays an essential role in both clinical and scientific understanding. Two of the common techniques to obtain these labels are through either fully automated segmentation or through multi-atlas based segmentation and label fusion. Fully automated techniques often result in highly accurate segmentations but lack the robustness to be viable in many cases. On the other hand, label fusion techniques are often extremely robust, but lack the accuracy of automated algorithms for specific classes of problems. Herein, we propose to perform simultaneous automated segmentation and statistical label fusion through the reformulation of a generative model to include a linkage structure that explicitly estimates the complex global relationships between labels and intensities. These relationships are inferred from the atlas labels and intensities and applied to the target using a non-parametric approach. The novelty of this approach lies in the combination of previously exclusive techniques and attempts to combine the accuracy benefits of automated segmentation with the robustness of a multi-atlas based approach. The accuracy benefits of this simultaneous approach are assessed using a multi-label multi- atlas whole-brain segmentation experiment and the segmentation of the highly variable thyroid on computed tomography images. The results demonstrate that this technique has major benefits for certain types of problems and has the potential to provide a paradigm shift in which the lines between statistical label fusion and automated segmentation are dramatically blurred.
医学成像中感兴趣结构的标记或分割在临床和科学理解方面都起着至关重要的作用。获取这些标记的两种常见技术是通过全自动分割或基于多图谱的分割与标记融合。全自动技术通常能产生高度准确的分割结果，但在许多情况下缺乏可行性所需的稳健性。另一方面，标记融合技术通常极其稳健，但对于特定类型的问题缺乏自动算法的准确性。在此，我们提议通过重新构建生成模型来同时进行自动分割和统计标记融合，该生成模型包含一个明确估计标记与强度之间复杂全局关系的链接结构。这些关系从图谱标记和强度中推断出来，并使用非参数方法应用于目标。这种方法的新颖之处在于将先前相互排斥的技术相结合，并试图将自动分割的准确性优势与基于多图谱方法的稳健性相结合。使用多标签多图谱全脑分割实验以及计算机断层扫描图像上高度可变甲状腺的分割来评估这种同步方法的准确性优势。结果表明，该技术对于某些类型的问题具有重大优势，并且有可能带来一种范式转变，即统计标记融合与自动分割之间的界限将被极大地模糊。
