Purpose

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.

Methods

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.

Results

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.

Conclusion

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.

中文翻译：

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用于非人类灵长类动物研究的基于图谱的肝脏分割。

目的

某些病毒感染性疾病会引起全身性损伤，肝脏是直接受病毒和/或宿主对病毒反应影响的重要器官。医学影像表明肝损伤是异质的，因此，对这些变化的量化需要对整个器官进行分析。在临床前成像研究中描绘肝脏是一项耗时且艰巨的任务，这将受益于自动肝脏分割。

方法

开发了一种基于非人类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。从正常和异常肝脏生成一组 82 次非人类灵长类动物的计算机断层扫描 (CT) 扫描，其中包含描绘肝脏的相关手动轮廓。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 距离度量和 Bland-Altman 图评估了该技术。

结果

自动分割结果优于手动轮廓绘制，Dice 得分中位数为 0.91。Bland-Altman 图的一致性限制表明，使用我们的自动分割方法可以检测到 3 Hounsfield 单位 (CT) 和 0.4 SUVmean（正电子发射断层扫描）的肝脏变化，这远小于宿主对这些病毒的反应中观察到的变化。传染性疾病。

结论

提议的基于图谱的肝脏分割技术可推广到各种大小和种类的非人类灵长类动物，并促进临床前传染病研究。虽然使用的图像分析软件是商业可用的，并且有资金的设施可以访问该软件来执行类似的非人类灵长类动物肝脏定量分析，但该方法可以在开源框架中实施，因为这些方法没有任何专有内容。