Katzke Julian, Puchenkov Pavel, Stark Heiko, Economo Evan P
Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology Graduate University, Onna, 904-0495 Okinawa, Japan.
Scientific Computing and Data Analysis Section, Research Support Division, Okinawa Institute of Science and Technology Graduate University, Onna, 904-0495 Okinawa, Japan.
Integr Org Biol. 2022 Jan 31;4(1):obac001. doi: 10.1093/iob/obac001. eCollection 2022.
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.
骨骼肌负责动物全身的自主力量生成，而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与进化的关键一步。在羽状骨骼肌中，体积、纤维长度以及与力传递结构的附着角度构成了肌肉结构最相关的参数。通过断层扫描技术测量这些特征为繁琐且具有破坏性的解剖提供了一种替代方法，特别是随着断层扫描数据的可用性迅速增加。然而，需要简化的计算方法来高效获取这些信息。在这里，我们建立并比较了使用部分自动化图像分析的工作流程，以快速准确地估计动物肌肉结构。在通过分割分离出目标肌肉后，我们评估了免费和专有的纤维追踪算法来重建肌肉纤维。然后，我们展示了一个使用Blender Python API估计附着角度、纤维长度、肌肉体积和生理横截面积的脚本。我们将这些方法应用于昆虫和脊椎动物的肌肉，并提供了有指导的工作流程。与手动测量相比，纤维追踪的结果是一致的，但耗时要少得多。最后，我们强调了开源三维软件Blender作为可视化工具和可编写脚本的分析工具来处理数字化解剖数据的能力。在所有生物体中，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，这里介绍的工作流程和方法可以成为数字比较分析开源未来的一部分。
