Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Frederick, MD, USA.
Division of Clinical Research, Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA.
Int J Comput Assist Radiol Surg. 2020 Oct;15(10):1631-1638. doi: 10.1007/s11548-020-02225-9. Epub 2020 Jul 9.
Certain viral infectious diseases cause systemic damage and the liver is an important organ affected directly by the virus and/or the hosts' response to the virus. Medical imaging indicates that the liver damage is heterogenous, and therefore, quantification of these changes requires analysis of the entire organ. Delineating the liver in preclinical imaging studies is a time-consuming and difficult task that would benefit from automated liver segmentation.
A nonhuman primate atlas-based liver segmentation method was developed to support quantitative image analysis of preclinical research. A set of 82 computed tomography (CT) scans of nonhuman primates with associated manual contours delineating the liver was generated from normal and abnormal livers. The proposed technique uses rigid and deformable registrations, a majority vote algorithm, and image post-processing operations to automate the liver segmentation process. This technique was evaluated using Dice similarity, Hausdorff distance measures, and Bland-Altman plots.
Automated segmentation results compare favorably with manual contouring, achieving a median Dice score of 0.91. Limits of agreement from Bland-Altman plots indicate that liver changes of 3 Hounsfield units (CT) and 0.4 SUVmean (positron emission tomography) are detectable using our automated method of segmentation, which are substantially less than changes observed in the host response to these viral infectious diseases.
The proposed atlas-based liver segmentation technique is generalizable to various sizes and species of nonhuman primates and facilitates preclinical infectious disease research studies. While the image analysis software used is commercially available and facilities with funding can access the software to perform similar nonhuman primate liver quantitative analyses, the approach can be implemented in open-source frameworks as there is nothing proprietary about these methods.
某些病毒性传染病会导致全身损伤，而肝脏是直接受到病毒和/或宿主对病毒反应影响的重要器官。医学影像学表明，肝脏损伤具有异质性，因此，需要对整个器官进行量化分析。在临床前成像研究中，勾画肝脏是一项耗时且困难的任务，自动化肝脏分割将对此有所帮助。
开发了一种基于非人灵长类动物图谱的肝脏分割方法，以支持临床前研究的定量图像分析。从正常和异常肝脏中生成了一组 82 个非人灵长类动物的计算机断层扫描（CT）扫描，以及相关的手动轮廓来勾画肝脏。所提出的技术使用刚性和可变形配准、多数投票算法和图像后处理操作来自动化肝脏分割过程。使用 Dice 相似性、Hausdorff 距离测量和 Bland-Altman 图评估了该技术。
自动化分割结果与手动勾画相比表现良好，达到了中位数 Dice 得分为 0.91。Bland-Altman 图的一致性界限表明，使用我们的自动化分割方法可以检测到 3 个亨斯菲尔德单位（CT）和 0.4 SUVmean（正电子发射断层扫描）的肝脏变化，这明显小于宿主对这些病毒性传染病的反应中观察到的变化。
所提出的基于图谱的肝脏分割技术可推广应用于各种大小和物种的非人灵长类动物，并促进临床前传染病研究。虽然所使用的图像分析软件是商业可用的，并且有资金的机构可以访问该软件来执行类似的非人灵长类动物肝脏定量分析，但由于这些方法没有任何专有的内容，因此可以在开源框架中实现该方法。
