Department of Exercise Physiology, Faculty of Literature and Human Sciences, Lorestan University, Khoramabad, Iran.
Department of Computer Engineering, Lorestan University, Khorramabad, Iran.
Sci Rep. 2021 Oct 29;11(1):21327. doi: 10.1038/s41598-021-00886-3.
Skeletal muscle is an adaptive tissue with the ability to regenerate in response to exercise training. Cross-sectional area (CSA) quantification, as a main parameter to assess muscle regeneration capability, is highly tedious and time-consuming, necessitating an accurate and automated approach to analysis. Although several excellent programs are available to automate analysis of muscle histology, they fail to efficiently and accurately measure CSA in regenerating myofibers in response to exercise training. Here, we have developed a novel fully-automated image segmentation method based on neutrosophic set algorithms to analyse whole skeletal muscle cross sections in exercise-induced regenerating myofibers, referred as MyoView, designed to obtain accurate fiber size and distribution measurements. MyoView provides relatively efficient, accurate, and reliable measurements for CSA quantification and detecting different myofibers, myonuclei and satellite cells in response to the post-exercise regenerating process. We showed that MyoView is comparable with manual quantification. We also showed that MyoView is more accurate and efficient to measure CSA in post-exercise regenerating myofibers as compared with Open-CSAM, MuscleJ, SMASH and MyoVision. Furthermore, we demonstrated that to obtain an accurate CSA quantification of exercise-induced regenerating myofibers, whole muscle cross-section analysis is an essential part, especially for the measurement of different fiber-types. We present MyoView as a new tool to quantify CSA, myonuclei and satellite cells in skeletal muscle from any experimental condition including exercise-induced regenerating myofibers.
骨骼肌是一种适应性组织，能够在运动训练的刺激下再生。横截面积（CSA）的量化是评估肌肉再生能力的主要参数，这一过程非常繁琐和耗时，因此需要一种准确和自动化的分析方法。尽管有几个优秀的程序可以自动分析肌肉组织学，但它们无法有效地、准确地测量运动训练引起的肌纤维再生的 CSA。在这里，我们开发了一种新的基于 Neutrosophic 集算法的全自动图像分割方法，用于分析运动诱导的肌纤维再生中的整个骨骼肌横切面，我们称之为 MyoView，旨在获得准确的纤维大小和分布测量。MyoView 提供了相对高效、准确和可靠的 CSA 量化测量方法，并能检测到不同的肌纤维、肌细胞核和卫星细胞，以响应运动后的再生过程。我们表明，MyoView 的测量结果与手动量化结果相当。我们还表明，与 Open-CSAM、MuscleJ、SMASH 和 MyoVision 相比，MyoView 更准确、更高效地测量运动后再生肌纤维的 CSA。此外，我们还证明，为了准确地量化运动诱导的肌纤维再生，全肌肉横切面分析是必不可少的一部分，特别是对于不同纤维类型的测量。我们将 MyoView 作为一种新的工具，用于定量分析任何实验条件下的骨骼肌 CSA、肌细胞核和卫星细胞，包括运动诱导的肌纤维再生。
