Fully automated muscle quality assessment by Gabor filtering of second harmonic generation images

Abstract

Although structural changes on the sarcomere level of skeletal muscle are known to occur due to various pathologies, rigorous studies of the reduced sarcomere quality remain scarce. This can possibly be explained by the lack of an objective tool for analyzing and comparing sarcomere images across biological conditions. Recent developments in second harmonic generation (SHG) microscopy and increasing insight into the interpretation of sarcomere SHG intensity profiles have made SHG microscopy a valuable tool to study microstructural properties of sarcomeres. Typically, sarcomere integrity is analyzed by fitting a set of manually selected, one-dimensional SHG intensity profiles with a supramolecular SHG model. To circumvent this tedious manual selection step, we developed a fully automated image analysis procedure to map the sarcomere disorder for the entire image at once. The algorithm relies on a single-frequency wavelet-based Gabor approach and includes a newly developed normalization procedure allowing for unambiguous data interpretation. The method was validated by showing the correlation between the sarcomere disorder, quantified by the M-band size obtained from manually selected profiles, and the normalized Gabor value ranging from 0 to 1 for decreasing disorder. Finally, to elucidate the applicability of our newly developed protocol, Gabor analysis was used to study the effect of experimental autoimmune encephalomyelitis on the sarcomere regularity. We believe that the technique developed in this work holds great promise for high-throughput, unbiased, and automated image analysis to study sarcomere integrity by SHG microscopy.

Optical prediction of single muscle fiber force production using a combined biomechatronics and second harmonic generation imaging approach

“…Since similar patterns, i.e., wavy and disorganized sarcomeres, were also observed in dystrophic muscle, both in sections 20 and in single fibers 14 , quantitative morphometry approaches were subsequently developed by various groups 20 , 38 . In addition to Fourier-transform based analyses of sarcomere patterns 38 or single-frequency wavelet-based Gabor filtering to quantify structural disorder 22 , our group developed pattern recognition algorithms based on boundary tensor analysis into fully automated pattern extraction routines to define two major structural parameters of myofibrillar order, reflected by the CAS (a measure for myofibrillar parallelism) and the density of so-called verniers (a measure for out-of-register appearances of myofibrils) 14 , 23 – 25 . In various subsequent age-related studies applying this set of SHG morphometry tools to dystrophic mdx or R349P mutant desmin muscle, we established these morphometric parameters for structural diagnosis of ‘myopathy’ and for monitoring disease progression with age 24 – 27 .…”

“…Biomolecules susceptible for SHG comprise collagen-I and myosin-II, enabling label-free, detailed structural analysis of subcellular cytoarchitecture and myofibrillar geometry in 3D 3 , 20 , 21 . Furthermore, several mathematical analysis strategies have been developed to describe the degree of myofibrillar disarray using quantitative morphometry in 2D images 14 , 20 , 22 and in 3D volumes of muscle or single fiber SHG stacks 23 – 25 . Our group introduced boundary tensor orientation analysis of myofibrillar striation patterns to extract two very sensitive parameters of ‘myofibrillar disorder,’ namely, cosine angle sums (CAS) and vernier densities (VDs).…”

Association of Tumor-Associated Collagen Signature With Prognosis and Adjuvant Chemotherapy Benefits in Patients With Gastric Cancer

“… 38 Currently, a common consensus about what types of textural features should be included to represent collagen alterations has not yet been reached. However, the textural features mainly included first-order statistics (eg, histogram-based features), second-order statistics (eg, gray-level concurrence matrix–based features), and wavelet transformation (eg, Gabor wavelet transform features), 38 , 39 , 40 which have been proven to have powerful potential for disease diagnosis. Stromal collagen information could be robustly measured and quantified via the combination of morphologic and textural features.…”

“…Stromal collagen information could be robustly measured and quantified via the combination of morphologic and textural features. 41 Although most published studies 38 , 39 , 40 , 41 only assessed the correlation of textural features and patient outcomes, we assumed that there is an underlying molecular assignment for different textural features.…”