Abstract：
The CAS3D image processing method intuitively applies a combination of Fourier space and real space 3D analysis algorithms to volumetric images of single skeletal muscle fiber Myosin II Second Harmonic Generation (SHG) XYZ image data. Our developed tool automatically quantifies the myofibrillar orientation in muscle samples by determining the cosine angle sum of intensity gradients in 3D (CAS3D) while determining the mean sarcomere length (SL) and sample orientation. The expected CAS3D values could be reproduced from ideal artificial data sets. Applied random noise in artificial images lowers the detected CAS3D value, and for noise levels below 20%, the correlation can be approximated by a linear function with a slope of -0.006 CAS3D/noise%. The deviations in SL and orientation detection were determined on ideal and noisy artificial data sets and were statistically indistinguishable from 0 (null hypothesis t-test P > 0.1). The software was applied to a previously published data set of single skeletal muscle fiber volumetric SHG image data from a rat intensive care unit (ICU) model of ventilator-induced diaphragm dysfunction (VIDD) with treatment regimens involving the small anti-inflammatory molecules BGP-15, vamorolone, or prednisolone. Our method reliably reproduced the results of the previous work and improved the standard deviation of the cosine angle sum detection in all sample groups from a mean of 0.03 to 0.008. This improvement is achieved by applying analysis algorithms to the whole volumetric images in 3D in contrast to the previously common method of slice-wise XY analysis.
摘要：
CAS3D图像处理方法直观地将傅里叶空间和真实空间3D分析算法的组合应用于单个骨骼肌纤维肌球蛋白II二次谐波产生(SHG)XYZ图像数据的体积图像。我们开发的工具通过确定3D强度梯度的余弦角和（CAS3D），同时确定平均肌节长度（SL）和样品方向，自动量化肌肉样品中的肌原纤维方向。可以从理想的人工数据集再现预期的CAS3D值。在人工图像中应用随机噪声降低了检测到的CAS3D值，对于低于20%的噪音水平，相关性可以通过斜率为-0.006CAS3D/噪声%的线性函数来近似。SL和取向检测的偏差在理想和嘈杂的人工数据集上确定，并且在统计学上与0没有区别（零假设t检验P>0.1）。将该软件应用于先前发布的来自呼吸机诱发的diaphragm肌功能障碍（VIDD）的大鼠重症监护病房（ICU）模型的单个骨骼肌纤维体积SHG图像数据集，其治疗方案涉及小的抗炎分子BGP-15，vamorolone，或者泼尼松龙。我们的方法可靠地再现了先前工作的结果，并将所有样本组中余弦角和检测的标准偏差从0.03的平均值提高到0.008。与先前常见的逐层XY分析方法相比，通过将分析算法应用于3D中的整个体积图像来实现这种改进。
