CAS3D: 3D quantitative morphometry on Second Harmonic Generation image volumes from single skeletal muscle fibers

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. [Display omitted] •The orientation of A-bands in skeletal muscles helps in determining muscle health.•A-bands can be visualized label-free in 3D with multiphoton microscopy.•The cosine angle sum of A-band gradients in 3D quantifies muscle health.•The developed algorithm automatically extracts CAS3D from 3D Images.•The developed algorithm performs significantly better than previous solutions.