A Roadmap to Reconstructing Muscle Architecture from CT Data

Abstract

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 3D 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.

A biomechanical model for the relation between bite force and mandibular opening angle in arthropods

“…x 2 1 ¼ 8:53, p < 0.01)-it remained small even for large opening angles, , 3 . The quality of our simple sorting algorithm thus compares favourably to that of state-of-the-art commercial tracing software [109,117].…”

A biomechanical model for the relation between bite force and mandibular opening angle in arthropods

“…Although this error changed significantly with opening angle – pennation angles were increasingly underestimated at larger opening angles [, p < 0.01] – it remained small even for large opening angles, < 3°. The quality of our simple sorting algorithm thus compares favourably to that of state-of-the-art commercial tracing software [105, 112].…”

New frontiers in imaging, anatomy, and mechanics of crocodylian jaw muscles

“…Most importantly, it saves time and is generally holistic, capturing all available fascicles in one sample in just a few hours of computational work whereas handsegmenting thousands of individual fascicles, in our alligator, for example, might take hundreds of person-hours of effort. That said, determining how well-automated methods capture true morphology is still under study by a variety of researchers (Dickinson et al 2018;Khalife et al, 2018;Sullivan et al, 2019;Dickinson et al, 2019;Katzke et al, 2022) and even the data presented here (Figure 8) are worthy of further refinement. Noise is introduced by specimen preservation, preparation, imaging parameters, machine learning parameters, connective tissue, interweaving neurovasculature, and other sources.…”

“…For example, a hatchling alligator head (MUVC AL31) featured in Holliday et al, (2013), Sellers et al, (2017) and Lessner and Holliday (2020) has proven resilient to re‐immersing and re‐imaging over the course of years as microCT resolution and specimen fit has increased from ~82 to ~23 μm (Figures 1e, f, 4). New details of the same specimen are now revealed by the increased resolution and so finer branches of the same nerves can be segmented (Lessner and Holliday 2020), the muscles are now more amenable to 3D fascicle tracking (e.g., Jeffery et al, 2011; Kupczik et al, 2015; Dickinson et al, 2018; Sullivan et al, 2019; Katzke et al, 2022; Figures 7, 8), and the overall accuracy of segmented structures is increased.…”

“…But despite their primacy, these methods can be destructive or otherwise modify the muscles under study including separation of connective tissues and muscle fibers from their attachments, displacement of neurovasculature and other elements and ultimately removal or destruction of tissues, precluding the study of, for example, museum specimens of rare or endangered species. Contrast imaging offers insights into many of these same gross features in situ and like dissection‐based approaches, enables the digital isolation of soft‐tissue structures and measurements of them including surface areas of attachments (Grosse et al, 2007; Davis et al, 2010; Sellers et al, 2017; Cost et al, 2020), volumes (Holliday et al, 2013; Lautenschlager 2014), muscle arrangement (Lautenschlager et al, 2017), and potentially properties of muscle architecture (Dickinson et al 2018; Sullivan et al, 2019; Katzke et al, 2022; Figures 7, 8).…”