A Roadmap to Reconstructing Muscle Architecture from CT Data
A Roadmap to Reconstructing Muscle Architecture from CT Data
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DOI：
10.1093/iob/obac001
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发表时间：
2022-02-18
影响因子：
1.5
通讯作者：
Economo, Evan P.
中科院分区：
文献类型：
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作者：
Katzke, Julian;Puchenkov, Pavel;Economo, Evan P.
骨骼肌负责动物自发力的产生，肌肉结构在很大程度上决定了机械输出的参数。因此，通过肌肉结构分析肌肉性能的能力是更好地理解运动和形态的生态学和进化的关键一步。在pennate骨骼肌中，体积、纤维长度和与力传递结构的附着角度构成了肌肉结构最相关的参数。通过层析成像技术测量这些特征提供了一种替代繁琐和破坏性的解剖，特别是随着层析成像数据的可用性迅速增加。然而，需要简化的计算方法来有效地访问这些信息。在这里，我们使用部分自动化图像分析来建立和比较工作流程，以快速准确地估计动物肌肉结构。在通过分割分离目标肌肉后，我们评估了自由可用的和专有的纤维跟踪算法来重建肌肉纤维。然后，我们展示了一个脚本，使用Blender Python API来估计附着角、纤维长度、肌肉体积和生理横截面积。我们将这些方法应用于昆虫和脊椎动物肌肉，并提供指导工作流程。与人工测量相比，纤维示踪的结果是一致的，但耗时少得多。最后，我们强调了开源三维软件Blender作为可视化工具和可脚本化分析工具来处理数字化解剖数据的能力。在整个生物体中，通过利用扫描的空间特征和肌肉纤维的几何特性，从断层扫描数据中提取、分析和可视化肌肉结构是可行的。随着解剖学数字图书馆的不断发展，这里提出的工作流程和方法可以成为数字比较分析的开源未来的一部分。
Synopsis Skeletal muscle is responsible for voluntary force generation across animals, and muscle architecture largely determines the parameters of mechanical output. The ability to analyze muscle performance through muscle architecture is thus a key step towards better understanding the ecology and evolution of movements and morphologies. In pennate skeletal muscle, volume, fiber lengths, and attachment angles to force transmitting structures comprise the most relevant parameters of muscle architecture. Measuring these features through tomographic techniques offers an alternative to tedious and destructive dissections, particularly as the availability of tomographic data is rapidly increasing. However, there is a need for streamlined computational methods to access this information efficiently. Here, we establish and compare workflows using partially automated image analysis for fast and accurate estimation of animal muscle architecture. After isolating a target muscle through segmentation, we evaluate freely available and proprietary fiber tracing algorithms to reconstruct muscle fibers. We then present a script using the Blender Python API to estimate attachment angles, fiber lengths, muscle volume, and physiological cross-sectional area. We apply these methods to insect and vertebrate muscle and provide guided workflows. Results from fiber tracing are consistent compared to manual measurements but much less time-consuming. Lastly, we emphasize the capabilities of the open-source three-dimensional software Blender as both a tool for visualization and a scriptable analytic tool to process digitized anatomical data. Across organisms, it is feasible to extract, analyze, and visualize muscle architecture from tomography data by exploiting the spatial features of scans and the geometric properties of muscle fibers. As digital libraries of anatomies continue to grow, the workflows and approach presented here can be part of the open-source future of digital comparative analysis.