SMASH – semi-automatic muscle analysis using segmentation of histology: a MATLAB application
© Smith and Barton; licensee BioMed Central Ltd. 2014
Received: 24 August 2014
Accepted: 15 October 2014
Published: 27 November 2014
Histological assessment of skeletal muscle tissue is commonly applied to many areas of skeletal muscle physiological research. Histological parameters including fiber distribution, fiber type, centrally nucleated fibers, and capillary density are all frequently quantified measures of skeletal muscle. These parameters reflect functional properties of muscle and undergo adaptation in many muscle diseases and injuries. While standard operating procedures have been developed to guide analysis of many of these parameters, the software to freely, efficiently, and consistently analyze them is not readily available. In order to provide this service to the muscle research community we developed an open source MATLAB script to analyze immunofluorescent muscle sections incorporating user controls for muscle histological analysis.
The software consists of multiple functions designed to provide tools for the analysis selected. Initial segmentation and fiber filter functions segment the image and remove non-fiber elements based on user-defined parameters to create a fiber mask. Establishing parameters set by the user, the software outputs data on fiber size and type, centrally nucleated fibers, and other structures. These functions were evaluated on stained soleus muscle sections from 1-year-old wild-type and mdx mice, a model of Duchenne muscular dystrophy. In accordance with previously published data, fiber size was not different between groups, but mdx muscles had much higher fiber size variability. The mdx muscle had a significantly greater proportion of type I fibers, but type I fibers did not change in size relative to type II fibers. Centrally nucleated fibers were highly prevalent in mdx muscle and were significantly larger than peripherally nucleated fibers.
The MATLAB code described and provided along with this manuscript is designed for image processing of skeletal muscle immunofluorescent histological sections. The program allows for semi-automated fiber detection along with user correction. The output of the code provides data in accordance with established standards of practice. The results of the program have been validated using a small set of wild-type and mdx muscle sections. This program is the first freely available and open source image processing program designed to automate analysis of skeletal muscle histological sections.
KeywordsHistological muscle analysis Standardized quantitative analysis Image segmentation mdx mouse
Skeletal muscle has a robust ability to adapt to the pattern of use and to regenerate following injury. These are often quantified using histological techniques. However, the methods for this quantification remain disparate among investigators and often require painstaking manual procedures [1, 2]. The goal of this work is to provide a widely available image processing software package specifically designed for muscle histological analysis.
Altering muscle fiber size is one of the primary methods in which muscle responds to external stimuli. Muscle mass may be increased in response to resistance training  or with potential pharmacological agents like myostatin inhibitors , while muscle atrophy occurs in response to disuse  and injuries such as denervation . These conditions primarily reflect hypertrophy or atrophy of individual fibers rather than hyper- or hypoplasia . Muscle fiber size is routinely evaluated using fixed or frozen tissue sections. Fiber outlines are visualized using a variety of techniques, including hematoxylin and eosin staining, laminin immunostaining, dystrophin immunostaining, and wheat germ aggluttinin staining . While these techniques enable visualization of fiber boundaries, determining fiber cross-sectional area (CSA) is often still performed by manual tracing of individual fibers. There are software programs available to help automate fiber detection, however they are often expensive and are not specifically designed for muscle histology .
Muscle fiber type distributions are often investigated in muscle histology as they are known to be altered in response to exercise, inactivity, and aging . Fiber type is primarily determined by the myosin heavy chain isoform, which has differential contractile and ATPase activity. Fiber type is often determined by ATPase staining [11, 12] or with immunostaining for specific myosin heavy chain (MyHC) isoforms individually [13, 14]. However, methods to determine fiber type can be subjective and tedious when fibers are manually classified. Following fiber segmentation, computing the size distribution of single fiber types is easily automated.
Muscle fibers also undergo changes in morphology as they develop. In particular, centrally nucleated fibers are often used as a marker for muscle regeneration. While fully mature fibers have peripheral nuclei, newly regenerated fibers have central nuclei . In many muscular dystrophies, which are characterized by continual cycles of degeneration and regeneration, the number of centrally nucleated fibers (CNFs) is substantial while CNFs are hardly present in healthy muscle. Although nuclei are easily stained with DAPI, determination of CNFs is often performed manually. Combined use of automated CNF and fiber size determination allows the size of regenerating fibers to be calculated, providing a measure of how efficiently regeneration is occurring after acute injury .
Skeletal muscle is a highly metabolically active tissue requiring large blood supply. As with fiber type shifts, capillary density of skeletal muscle may be affected by altered metabolic demand or in disease . Endothelial cells and capillaries are frequently stained in skeletal muscle with Von Willibrand Factor or PECAM [18, 19]. Automated determination of capillary density in relation to fiber size and number provides a useful parameter of skeletal muscle histology.
All of the methods discussed above are commonly performed using immunofluorescence, which provides high contrast in stained and unstained structures. We have developed MATLAB (MATLAB and Image Processing Toolbox 2014a, MathWorks) scripts bundled into a MATLAB App (see Availability and Requirements) that automate, or partially automate determination of fiber size, fiber type, centrally nucleated fibers, and capillary density. These programs are created to comply with standard operating procedures developed by TREAT-NMD when available using sophisticated boundary detection algorithms . The software also includes built-in image editing to manually inspect and manipulate fiber boundaries. Fully automated fiber size determination as well as fiber types and CNFs may be possible with adequate image acquisition [9, 20]. However, these newly designed fully automated programs are not yet available  and/or have a significant cost . Additionally, allowing the user to have manual control over some aspect of image processing allows users to maintain the fidelity established by manual techniques. The open nature of this software also allows custom usage and further advancement of the methods. For users that do not have access to a MATLAB license or the image processing toolbox we have compiled an .exe file that runs using the freely available MATLAB Runtime Compiler (MCR) version 8.3 (http://www.mathworks.com/products/compiler/mcr/). Automating a large portion of muscle histology makes it feasible to analyze full muscle cross-sections, eliminating variability introduced by selecting only a portion of the cross section for analysis. This software is validated with muscles from mdx mice, which have many alterations of muscle fiber morphology compared to wild-type mice . The purpose of this study is to develop freely available automatic and standardized image segmentation platform and validate the program using standard muscle histological analysis.
All animal experiments were approved by the University of Pennsylvania Institutional Animal Care and Use Committee. C57Bl/6 mice were used as wild-type controls and mdx mice were used as a dystrophic model. Both animal groups were analyzed at 1 year of age.
Soleus muscles from both groups (n = 4 per group) were dissected, embedded in OCT, and frozen in liquid nitrogen cooled isopentane. Frozen 10 um sections were cut from muscles and mounted on slides. Sections were washed in PBS and immunostained using antibodies to laminin (Thermo Scientific), and either myosin heavy chain I (Developmental Studies Hybridoma Bank) or platelet endothelial cell adhesion molecule (PECAM; eBiosciences) overnight at 4°C. After PBS wash, fluorescent secondary antibodies (Sigma) were applied for 1 h at room temperature. Nuclei were labeled with DAPI incorporated into the mounting media (Vectashield). Images were acquired using a Leica DM RBE microscope and DFC350FX camera and OpenLab software. Individual fields were stitched together to create a composite full view of the muscle cross-section using Photoshop (Adobe).
Default parameters from Excel file
Pixel size (μm/pixel)
Fiber outline channel (red = 1, green = 2, blue = 3)
Nuclei channel (red = 1, green = 2, blue = 3)
Fiber type channel (red = 1, green = 2, blue = 3)
Object channel (red = 1, green = 2, blue = 3)
Segmentation smoothing factor
Nuclear smoothing factor
Object smoothing factor
Minimum fiber area (μm2)
Maximum fiber area (μm2)
Nuclear distance from boarder (μm)
Minimum nuclear size (μm2)
Fiber properties output folder
Fiber type output folder
Central nuclei output folder
Objects output folder
Centrally nucleated fibers
Capillary density or non-fiber objects
SMASH data verification
To verify the data generated by SMASH we compared the same images used in previously published data . In this case legacy methods were done using Openlab software (Improvision, PerkinElmer) with simple thresholding of individual RGB channels for fiber size and fiber type. For central nucleation fibers were manually determined to be CNF or PNF and a marker placed using Openlab to enable counting.
The C57 and mdx output data were compared using a Student’s t-test. For comparing CNFs and PNFs a paired Student’s t-test was used. All data analyses were performed using PRISM (Graphpad Software).
Fiber type determination is commonly performed in muscle histological analysis. The muscles studied here have been stained for slow myosin heavy chain (Type I). The soleus muscle has a large portion of type I fibers of approximately 40% in wild-type mice . This proportion is close to the value obtained with the software (Figure 9E), however there was a large increase in the percentage of slow fibers in the mdx soleus muscle. This is expected based on the advanced age of these mice of 1 year and that type I fibers are more resistant to damage in mdx muscle . Determination of the size of various fiber types is an automatic feature of the software. Assuming all unlabeled fibers are type II fibers the results show that C57 type I fibers are smaller than type II and the relationship is not different in mdx soleus muscles (Figure 9F).
Due to the continual cycles of degeneration and regeneration, dystrophic muscles have a high prevalence of CNFs, while uninjured wild-type muscles have very few CNFs. The percentage of CNFs in dystrophic muscle is a commonly measured histological marker of disease . The automatic detection demonstrated the expected results with C57 soleus muscle showing very rare CNFs while >30% of mdx soleus muscle fibers were CNFs [33, 35] (Figure 9G). While CNF percentage is routinely performed, often using manual methods, reporting on the size of the CNFs is less common. The software makes it trivial to measure the size of the CNF population. The data collected show that within mdx muscle CNFs are larger than peripherally nucleated fibers (PNFs) (Figure 9H). This relationship has been previously observed in the EDL muscle of mdx mice [16, 36].
Data comparison from legacy methods to SMASH
Fiber type - myosin IIa (%)
Fiber size - fiber area (μm2)
Fiber size - fiber area SD (μm2)
Centrally nucleated fibers (%)
Run time (mm:ss) comparisons from legacy methods to SMASH
Legacy method - functions
SMASH - functions
The manual analysis of skeletal muscle immunofluorescence is often a laborious task. The Semiautomatic Image Processing of Skeletal Muscle Histology Software described and tested provides researchers with a valuable tool for measuring multiple facets of muscle histology. While image analysis software is available to conduct many of these features, it carries a high monetary cost and is not specifically designed for skeletal muscle analysis. In contrast, this program is available on the widely used MATLAB software and is designed to investigate specific features of muscle histology, and is open to custom modifications for advanced users.
All of the functions in the program rely and are based on the segmentation of skeletal muscle fibers in an image. This is commonly done with manual tracing, which is very time-consuming, or basic thresholding of fiber outlines, which requires extensive manual correction on all but the most pristine sections. Using a combination of the image smoothing fiber and the watershed transform fiber boundaries are automatically produced with much improved reliability over standard thresholding. However, the software maintains the manual ability to correct image segmentation. Furthermore, if the built-in manual editing features are not suitable, the user may edit the created mask file with software of his choice, such as Adobe Photoshop. After the mask has been finalized, running the analysis functions is expedient. The user is required to enter parameters for analysis and may adjust them based on the resulting image guides. To analyze the entire cross-section of 1-year-old soleus muscles for fiber size, fiber type, and CNFs from the same image took approximately 15 min, substantially less than manual methods. Increased time efficiency allows analysis of the whole muscle cross-section, avoiding the sampling issues of only using select fields of view. Using the whole muscle cross-section is required to meet TREAT-NMD standards of practice . Analyzing more fiber and eliminating regional differences in cross-section improves the consistency of the results. Highly significant results were obtained with low variability in our investigation with only four muscles per group.
The output of fiber size data in Excel format permits the use of many graphing tools to create plots of fiber size commonly used. Ensuring the ability to measure Feret diameter ensures compliance with the latest standard operating procedures for fiber size analysis . Automation of fiber type data with defined boundaries has advantages over using thresholding to determine fiber type and size. Instead of determining the fiber size by the area above a threshold value of staining, the area is determined entirely by the fiber outline so the positive fiber size will not be affected by blotchiness in fiber type staining. The fiber type function may also be used for additional signals internal to fibers such as Evan’s Blue Dye permeability or IgG infiltration of necrotic fibers [2, 38]. Automatic determination of CNFs provides a useful measure of skeletal muscle histology. Although it is not frequently studied, combining fiber size with CNF determination may provide a more informative marker of muscle health . The computation of this additional parameter is trivial using this software. Analysis of structures outside of fibers themselves in skeletal muscle is also important. The object counter function was designed to automate analysis of capillary density, as is done in muscle histological analysis for capillaries per fiber and capillaries per area . However, it may also be used to analyze a multitude of other structures within a muscle, such macrophage infiltration or matrix proteins.
The data generated by SMASH are validated against legacy methods showing largely consistent results between methods for fiber type and CNF percentages (Table 2). However, the discrepancy in fiber area illustrates the importance of using a single analytical method for a given study and highlights the variability between different approaches. Using a simple threshold to separate fibers creates highly variable borders between adjacent fibers that are dependent on parameters such as exposure time and focus during image acquisition. Filtering the signal and using the watershed function as is done in SMASH provides signal that is more robust to these parameters. SMASH provides a mask more consistent with manual tracing of fibers than applying a threshold. While SMASH reduces the border region between adjacent fibers, it is also clearly capable of delineating interstitial space between adjacent fibers when there is an appreciable separation as evidenced by the white and grey areas in Figure 10. Thus, we attest that SMASH generally provides a more robust and accurate fiber size as well as requiring less manual editing than using simple thresholds. It is also noteworthy that fiber area is generally more variable than Feret diameter as for perfectly round areas it is squaring the difference and the more elongated the fiber the greater the proportion of border region that may influence the results.
In addition the gains in robust analysis Table 3 demonstrates that SMASH greatly reduces the time required to analyze images. Manual editing of the fiber mask is still required in the majority of muscle sections for both initial segmentation and fiber filtering and take the majority of the processing time. However, with SMASH this manual editing is reduced to just a few minutes in the case of the soleus muscles tested. The time gains are especially significant when doing multiple analyses on the same image, as manually editing the mask is the major time consumer and additional functions are able to be processed in just seconds.
While this software provides many advantages there are notable limitations. The analysis is currently limited to immunofluorescence and is not compatible with common stains such as hematoxylin and eosin. The software does often require manual segmentation and filtration of fibers based on the current algorithms. These manual adjustments allow the user more control over the analysis, but also increase the time required for analysis and introduce a degree of subjectivity. Manual adjustments are more frequently required in analysis of diseased muscle as well. While these algorithms are advanced compared to many of the techniques currently in use, extending fiber segmentation algorithms may provide more reliable boundaries. The proposed method of measuring fiber type is limited to investigation of a single fiber type per image, or per color in an RGB image. Thus to measure each fiber type individually requires multiple image segmentation masks from serial sections. Alternatively, using distinguishable fluorophors for each fiber type  would permit the analysis of two fiber types using the same mask using this software. Using serial sections for fiber typing is standard procedure in many labs and it is not enhanced by this software. The analysis of myonuclei is limited to CNFs in this software, as opposed to providing a measure of myonuclear density with peripheral nuclei [9, 20]. The method of measuring CNFs is recommended by TREAT-NMD, however it causes an issue with very small fibers as the entire CSA may be in the defined border region, making it impossible to be labeled as a CNF. The measurement of non-fiber objects designed for capillary density is currently preliminary. There is no filtering of objects or a method to select an object of interest. However, the output of object size does allow filtering based on the objects CSA within Excel. This program currently is designed for use with muscle cross-sections and not designed to analyze images from longitudinal muscle sections. As an open source program, users may address any of these limitations as they see fit within the framework of the software platform and MATLAB.
The software package based in MATLAB provides image processing tools to analyze immunofluorescent muscle cross-sections. The semi-automatic fiber segmentation functions provide advanced algorithms for fiber segmentation as well as provide an interface for users to manually correct any errors. The histological analysis includes functions for fiber CSA, fiber Feret diameter, fiber typing, CNFs, and capillary density. These functions produced expected results comparing wild-type and dystrophic mouse muscle. These functions may be purposed for other analyses. This open source platform provides users a framework to create their own functions or modification of previously incorporated functions. Automated functions improve the speed and consistency of skeletal muscle histological analysis. Although it requires a MATLAB license, this is the only freely available software designed for the analysis of skeletal muscle histology.
Availability and requirements
Project name: SMASH - Semiautomatic Image Processing of Skeletal Muscle Histology: a MATLAB Application.
Project homepage: http://dx.doi.org/10.6084/m9.figshare.1247634
Operating System: Platform Independent.
Programming Language: MATLAB.
Other requirements: SMASH Stand Alone (SMASH_Installer.exe) requires MATLAB Compiler Runtime (MCR) version R2014a (8.3) which is freely available from Mathworks (http://www.mathworks.com/products/compiler/mcr/).
SMASH App (SMASH_App.mlappinstall) requires MATLAB version R2014a (8.3) or later with the Image Processing Toolbox.
Any restrictions to use by non-academics: None.
Centrally nucleated fiber
Extensor digitorum longus
Platelet endothelial cell adhesion molecule
Peripherally nucleated fiber
Red, Green, and Blue image format.
We would like to acknowledge our funding sources including support from the Muscle Core Facilities and Training from the Wellstone Muscle Physiology Core (AR052646) and support from the Pennsylvania Muscle Institute Training Fellowship (AR053461). We would like to acknowledge members of the Barton Lab for beta testing the software. We would also like to acknowledge Dr. Gretchen Meyer for providing technical expertise in MATLAB programming.
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