Automated image segmentation of haematoxylin and eosin stained skeletal muscle cross-sections

Improving quantification of myotube width and nuclear/cytoplasmic ratio in myogenesis research

BACKGROUND AND OBJECTIVE

The culture of skeletal muscle cells is particularly relevant to basic biomedical research and translational medicine. The incubation of dissociated cells under controlled conditions has helped to dissect several molecular mechanisms associated with muscle cell differentiation, in addition to contributing for the evaluation of drug effects and prospective cell therapies for patients with degenerative muscle pathologies. The formation of mature multinucleated myotubes is a stepwise process involving well defined events of cell proliferation, commitment, migration, and fusion easily identified through optical microscopy methods including immunofluorescence and live cell imaging. The characterization of each step is usually based on muscle cell morphology and nuclei number, as well as the presence and intracellular location of specific cell markers. However, manual quantification of these parameters in large datasets of images is work-intensive and prone to researcher's subjectivity, mostly because of the extremely elongated cell shape of large myotubes and because myotubes are multinucleated.

METHODS

Here we provide two semi-automated ImageJ macros aimed to measure the width of myotubes and the nuclear/cytoplasmic localization of molecules in fluorescence images. The width measuring macro automatically determines the best angle, perpendicular to most cells, to draw a profile plot and identify and measure individual myotubes. The nuclear/cytoplasmic ratio macro compares the intensity values along lines, drawn by the user, over cytoplasm and nucleus.

RESULTS

We show that the macro measurements are more consistent than manual measurements by comparing with our own results and with the literature.

CONCLUSIONS

By relying on semi-automated muscle specific ImageJ macros, we seek to improve measurement accuracy and to alleviate the laborious routine of counting and measuring muscle cell features.

Histological Methods to Assess Skeletal Muscle Degeneration and Regeneration in Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a progressive disease caused by the loss of function of the protein dystrophin. This protein contributes to the stabilisation of striated cells during contraction, as it anchors the cytoskeleton with components of the extracellular matrix through the dystrophin-associated protein complex (DAPC). Moreover, absence of the functional protein affects the expression and function of proteins within the DAPC, leading to molecular events responsible for myofibre damage, muscle weakening, disability and, eventually, premature death. Presently, there is no cure for DMD, but different treatments help manage some of the symptoms. Advances in genetic and exon-skipping therapies are the most promising intervention, the safety and efficiency of which are tested in animal models. In addition to in vivo functional tests, ex vivo molecular evaluation aids assess to what extent the therapy has contributed to the regenerative process. In this regard, the later advances in microscopy and image acquisition systems and the current expansion of antibodies for immunohistological evaluation together with the development of different spectrum fluorescent dyes have made histology a crucial tool. Nevertheless, the complexity of the molecular events that take place in dystrophic muscles, together with the rise of a multitude of markers for each of the phases of the process, makes the histological assessment a challenging task. Therefore, here, we summarise and explain the rationale behind different histological techniques used in the literature to assess degeneration and regeneration in the field of dystrophinopathies, focusing especially on those related to DMD.

On the Sufficient Condition for Solving the Gap-Filling Problem Using Deep Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) are routinely used for image segmentation of biomedical data sets to obtain quantitative measurements of cellular structures like tissues. These cellular structures often contain gaps in their boundaries, leading to poor segmentation performance when using DCNNs like the U-Net. The gaps can usually be corrected by post-hoc computer vision (CV) steps, which are specific to the data set and require a disproportionate amount of work. As DCNNs are Universal Function Approximators, it is conceivable that the corrections should be obsolete by selecting the appropriate architecture for the DCNN. In this article, we present a novel theoretical framework for the gap-filling problem in DCNNs that allows the selection of architecture to circumvent the CV steps. Combining information-theoretic measures of the data set with a fundamental property of DCNNs, the size of their receptive field, allows us to formulate statements about the solvability of the gap-filling problem independent of the specifics of model training. In particular, we obtain mathematical proof showing that the maximum proficiency of filling a gap by a DCNN is achieved if its receptive field is larger than the gap length. We then demonstrate the consequence of this result using numerical experiments on a synthetic and real data set and compare the gap-filling ability of the ubiquitous U-Net architecture with variable depths. Our code is available at https://github.com/ai-biology/dcnn-gap-filling.

Oral fucoidan improves muscle size and strength in mice

Fucoidan is a sulfated polysaccharide found in a range of brown algae species. Growing evidence supports the long-term supplementation of fucoidan as an ergogenic aid to improve skeletal muscle performance. The aim of this study was to investigate the effect of fucoidan on the skeletal muscle of mice. Male BL/6 mice (N = 8-10) were administered a novel fucoidan blend (FUC, 400 mg/kg/day) or vehicle (CON) for 4 weeks. Treatment and control experimental groups were further separated into exercise (CON+EX, FUC+EX) or no-exercise (CON, FUC) groups, where exercised groups performed 30 min of treadmill training three times per week. At the completion of the 4-week treatment period, there was a significant increase in cross-sectional area (CSA) of muscle fibers in fucoidan-treated extensor digitorum longus (EDL) and soleus fibers, which was accompanied by a significant increase in tibialis anterior (TA) muscle force production in fucoidan-treated groups. There were no significant changes in grip strength or treadmill time to fatigue, nor was there an effect of fucoidan or exercise on mass of TA, EDL, or soleus muscles. In gastrocnemius muscles, there was no change in mRNA expression of mitochondrial biogenesis markers PGC-1α and Nrf-2 in any experimental groups; however, there was a significant effect of fucoidan supplementation on myosin heavy chain (MHC)-2x, but not MHC-2a, mRNA expression. Overall, fucoidan increased muscle size and strength after 4 weeks of supplementation in both exercised and no-exercised mice suggesting an important influence of fucoidan on skeletal muscle physiology.

Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle

Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger’s line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed segmentation. Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~92.4%. The software has been tested to work on full muscle cross-sections and works well even under non-optimal staining conditions. The MyoSAT software tool has been implemented as a macro for the freely available ImageJ software platform. This new segmentation tool allows scientists to efficiently analyze large muscle cross-sections for use in research studies and diagnostics.

Muscle percentage index as a marker of disease severity in golden retriever muscular dystrophy

INTRODUCTION

Golden retriever muscular dystrophy (GRMD) is a spontaneous X-linked canine model of Duchenne muscular dystrophy that resembles the human condition. Muscle percentage index (MPI) is proposed as an imaging biomarker of disease severity in GRMD.

METHODS

To assess MPI, we used MRI data acquired from nine GRMD samples using a 4.7 T small-bore scanner. A machine learning approach was used with eight raw quantitative mapping of MRI data images (T1m, T2m, two Dixon maps, and four diffusion tensor imaging maps), three types of texture descriptors (local binary pattern, gray-level co-occurrence matrix, gray-level run-length matrix), and a gradient descriptor (histogram of oriented gradients).

RESULTS

The confusion matrix, averaged over all samples, showed 93.5% of muscle pixels classified correctly. The classification, optimized in a leave-one-out cross-validation, provided an average accuracy of 80% with a discrepancy in overestimation for young (8%) and old (20%) dogs.

DISCUSSION

MPI could be useful for quantifying GRMD severity, but careful interpretation is needed for severe cases.

SLCV-a supervised learning-computer vision combined strategy for automated muscle fibre detection in cross-sectional images

Muscle fibre cross-sectional area (CSA) is an important biomedical measure used to determine the structural composition of skeletal muscle, and it is relevant for tackling research questions in many different fields of research. To date, time consuming and tedious manual delineation of muscle fibres is often used to determine the CSA. Few methods are able to automatically detect muscle fibres in muscle fibre cross-sections to quantify CSA due to challenges posed by variation of brightness and noise in the staining images. In this paper, we introduce the supervised learning-computer vision combined pipeline (SLCV), a robust semi-automatic pipeline for muscle fibre detection, which combines supervised learning (SL) with computer vision (CV). SLCV is adaptable to different staining methods and is quickly and intuitively tunable by the user. We are the first to perform an error analysis with respect to cell count and area, based on which we compare SLCV to the best purely CV-based pipeline in order to identify the contribution of SL and CV steps to muscle fibre detection. Our results obtained on 27 fluorescence-stained cross-sectional images of varying staining quality suggest that combining SL and CV performs significantly better than both SL-based and CV-based methods with regards to both the cell separation- and the area reconstruction error. Furthermore, applying SLCV to our test set images yielded fibre detection results of very high quality, with average sensitivity values of 0.93 or higher on different cluster sizes and an average Dice similarity coefficient of 0.9778.

Towards Fine Whole-Slide Skeletal Muscle Image Segmentation through Deep Hierarchically Connected Networks

Automatic skeletal muscle image segmentation (MIS) is crucial in the diagnosis of muscle-related diseases. However, accurate methods often suffer from expensive computations, which are not scalable to large-scale, whole-slide muscle images. In this paper, we present a fast and accurate method to enable the more clinically meaningful whole-slide MIS. Leveraging on recently popular convolutional neural network (CNN), we train our network in an end-to-end manner so as to directly perform pixelwise classification. Our deep network is comprised of the encoder and decoder modules. The encoder module captures rich and hierarchical representations through a series of convolutional and max-pooling layers. Then, the multiple decoders utilize multilevel representations to perform multiscale predictions. The multiscale predictions are then combined together to generate a more robust dense segmentation as the network output. The decoder modules have independent loss function, which are jointly trained with a weighted loss function to address fine-grained pixelwise prediction. We also propose a two-stage transfer learning strategy to effectively train such deep network. Sufficient experiments on a challenging muscle image dataset demonstrate the significantly improved efficiency and accuracy of our method compared with recent state of the arts.

High throughput automatic muscle image segmentation using parallel framework

BACKGROUND

Fast and accurate automatic segmentation of skeletal muscle cell image is crucial for the diagnosis of muscle related diseases, which extremely reduces the labor-intensive manual annotation. Recently, several methods have been presented for automatic muscle cell segmentation. However, most methods exhibit high model complexity and time cost, and they are not adaptive to large-scale images such as whole-slide scanned specimens.

METHODS

In this paper, we propose a novel distributed computing approach, which adopts both data and model parallel, for fast muscle cell segmentation. With a master-worker parallelism manner, the image data in the master is distributed onto multiple workers based on the Spark cloud computing platform. On each worker node, we first detect cell contours using a structured random forest (SRF) contour detector with fast parallel prediction and generate region candidates using a superpixel technique. Next, we propose a novel hierarchical tree based region selection algorithm for cell segmentation based on the conditional random field (CRF) algorithm. We divide the region selection algorithm into multiple sub-problems, which can be further parallelized using multi-core programming.

RESULTS

We test the performance of the proposed method on a large-scale haematoxylin and eosin (H &E) stained skeletal muscle image dataset. Compared with the standalone implementation, the proposed method achieves more than 10 times speed improvement on very large-scale muscle images containing hundreds to thousands of cells. Meanwhile, our proposed method produces high-quality segmentation results compared with several state-of-the-art methods.

CONCLUSIONS

This paper presents a parallel muscle image segmentation method with both data and model parallelism on multiple machines. The parallel strategy exhibits high compatibility to our muscle segmentation framework. The proposed method achieves high-throughput effective cell segmentation on large-scale muscle images.

Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data

Purpose

Cancers are almost always diagnosed by morphologic features in tissue sections. In this context, machine learning tools provide new opportunities to describe tumor immune cell interactions within the tumor microenvironment and thus provide phenotypic information that might be predictive for the response to immunotherapy.

Methods

We develop a machine learning approach using variational networks for joint image denoising and classification of tissue sections for melanoma, which is an established model tumor for immuno-oncology research. The manual annotation of real training data would require substantial user interaction of experienced pathologists for each single training image, and the training of larger networks would rely on a very large number of such data sets with ground truth annotation. To overcome this bottleneck, we synthesize training data together with a proper tissue structure classification. To this end, a stochastic data generation process is used to mimic cell morphology, cell distribution and tissue architecture in the tumor microenvironment. Particular components of this tool are random placement and rotation of a large number of patches for presegmented cell nuclei, a stochastic fast marching approach to mimic the geometry of cells and texture generation based on a color covariance analysis of real data. Here, the generated training data reflect a large range of interaction patterns.

Results

In several applications to histological tissue sections, we analyze the efficiency and accuracy of the proposed approach. As a result, depending on the scenario considered, almost all cells and nuclei which ought to be detected are actually marked as classified and hardly any misclassifications occur.

Conclusions

The proposed method allows for a computer-aided screening of histological tissue sections utilizing variational networks with a particular emphasis on tumor immune cell interactions and on the robust cell nuclei classification.

Myostatin inhibition using mRK35 produces skeletal muscle growth and tubular aggregate formation in wild type and TgACTA1D286G nemaline myopathy mice

Nemaline myopathy (NM) is a heterogeneous congenital skeletal muscle disease with cytoplasmic rod-like structures (nemaline bodies) in muscle tissue. While weakness in NM is related to contractile abnormalities, myofiber smallness is an additional abnormality in NM that may be treatable. We evaluated the effects of mRK35 (a myostatin inhibitor developed by Pfizer) treatment in the TgACTA1D286G mouse model of NM. mRK35 induced skeletal muscle growth that led to significant increases in animal bodyweight, forelimb grip strength and muscle fiber force, although it should be noted that animal weight and forelimb grip strength in untreated TgACTA1D286G mice was not different from controls. Treatment was also associated with an increase in the number of tubular aggregates found in skeletal muscle. These findings suggest that myostatin inhibition may be useful in promoting muscle growth and strength in Acta1-mutant muscle, while also further establishing the relationship between low levels of myostatin and tubular aggregate formation.

Reduction of Autophagic Accumulation in Pompe Disease Mouse Model Following Gene Therapy

BACKGROUND

Pompe disease is a fatal neuromuscular disorder caused by a deficiency in acid α-glucosidase, an enzyme responsible for glycogen degradation in the lysosome. Currently, the only approved treatment for Pompe disease is enzyme replacement therapy (ERT), which increases patient survival, but does not fully correct the skeletal muscle pathology. Skeletal muscle pathology is not corrected with ERT because low cation-independent mannose-6-phosphate receptor abundance and autophagic accumulation inhibits the enzyme from reaching the lysosome. Thus, a therapy that more efficiently targets skeletal muscle pathology, such as adeno-associated virus (AAV), is needed for Pompe disease.

OBJECTIVE

The goal of this project was to deliver a rAAV9-coGAA vector driven by a tissue restrictive promoter will efficiently transduce skeletal muscle and correct autophagic accumulation.

METHODS

Thus, rAAV9-coGAA was intravenously delivered at three doses to 12-week old Gaa-/- mice. 1 month after injection, skeletal muscles were biochemically and histologically analyzed for autophagy-related markers.

RESULTS

At the highest dose, GAA enzyme activity and vacuolization scores achieved therapeutic levels. In addition, resolution of autophagosome (AP) accumulation was seen by immunofluorescence and western blot analysis of autophagy-related proteins. Finally, mice treated at birth demonstrated persistence of GAA expression and resolution of lysosomes and APs compared to those treated at 3 months.

CONCLUSION

In conclusion, a single systemic injection of rAAV9-coGAA ameliorates vacuolar accumulation and prevents autophagic dysregulation.

A neural network approach to analyze cross-sections of muscle fibers in pathological images

Morphological characteristics of muscle fibers, such as their cross-sections, are important indicators of the health and function of the musculoskeletal system. However, manual analysis of muscle fiber morphology is a labor-intensive and time-consuming process that is prone to errors. Overall, the procedure involves high inter- and intra-observer variability. Therefore, it is desirable for biologists to have a tool that can produce objective and reproducible analysis for muscle fiber images. In this work, we propose a deep convolutional neural network (DCNN) followed by post-processing for detecting and measuring the cross-sections of muscle fibers. We evaluate three segmentation networks for muscle boundary segmentation: (1) U-net, (2) FusionNet, and (3) a customized FusionNet. The customized FusionNet, which had the highest Dice coefficient on the test set, was used for subsequent morphological analysis of the muscle fibers. The proposed method was tested on microscopic images of the tibialis anterior muscles of a pre-clinical model of muscular dystrophy. The dataset contained four mosaic images, totalling more than 3400 fibers. Because of the severity of muscle injury in this pre-clinical model, its muscle fiber images present a challenge for quantitative analysis for several reasons. First, the muscle fibers had inhomogeneous spatial distribution and very different sizes. Second, the membranes of the muscle fibers had uneven signal intensity due to the loss of a membrane protein. Third, the shapes of intact muscle fibers were very different. All these factors contributed to the difficulty of acquiring good training data in the first place. Despite these difficulties, we achieved an average muscle fiber overlay precision of 0.65 and an average recall of 0.49. In this context, overlaid fibers are defined as fibers that have one or more pixels overlaying in the manual and DCNN cross-section segmentation. For the overlaid fibers, the proposed method achieved excellent segmentation accuracy of 94% ± 10.26%, as measured by the Dice-Sorensen coefficient.

AMPKα1-LDH pathway regulates muscle stem cell self-renewal by controlling metabolic homeostasis

Control of stem cell fate to either enter terminal differentiation versus returning to quiescence (self-renewal) is crucial for tissue repair. Here, we showed that AMP-activated protein kinase (AMPK), the master metabolic regulator of the cell, controls muscle stem cell (MuSC) self-renewal. AMPKα1^-/- MuSCs displayed a high self-renewal rate, which impairs muscle regeneration. AMPKα1^-/- MuSCs showed a Warburg-like switch of their metabolism to higher glycolysis. We identified lactate dehydrogenase (LDH) as a new functional target of AMPKα1. LDH, which is a non-limiting enzyme of glycolysis in differentiated cells, was tightly regulated in stem cells. In functional experiments, LDH overexpression phenocopied AMPKα1^-/- phenotype, that is shifted MuSC metabolism toward glycolysis triggering their return to quiescence, while inhibition of LDH activity rescued AMPKα1^-/- MuSC self-renewal. Finally, providing specific nutrients (galactose/glucose) to MuSCs directly controlled their fate through the AMPKα1/LDH pathway, emphasizing the importance of metabolism in stem cell fate.

AIIMDs: An Integrated Framework of Automatic Idiopathic Inflammatory Myopathy Diagnosis for Muscle

Idiopathic inflammatory myopathy (IIM) is a common skeletal muscle disease that relates to weakness and inflammation of muscle. Early diagnosis and prognosis of different types of IIMs will guide the effective treatment. Interpretation of digitized images of the cross-section muscle biopsy, which is currently done manually, provides the most reliable diagnostic information. With the increasing volume of images, the management and manual interpretation of the digitized muscle images suffer from low efficiency and high interobserver variabilities. In order to address these problems, we propose the first complete framework of automatic IIM diagnosis system for the management and interpretation of digitized skeletal muscle histopathology images. The proposed framework consists of several key components: (1) Automatic cell segmentation, perimysium annotation, and nuclei detection; (2) histogram-based feature extraction and quantification; (3) content-based image retrieval to search and retrieve similar cases in the database for comparative study; and (4) majority voting-based classification to provide decision support for computer-aided clinical diagnosis. Experiments show that the proposed diagnosis system provides efficient and robust interpretation of the digitized muscle image and computer-aided diagnosis of IIM.

Generalizing cell segmentation and quantification

Background

In recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work.

Results

To make the cell segmentation work more convergent, we propose a framework that is able to segment different kinds of cells automatically and robustly in this paper. This framework evolves the previously proposed method in segmenting the muscle cells and generalizes it to be suitable for segmenting and quantifying a variety of cell images by adding more union cases. Compared to the previous methods, the segmentation and quantification accuracy of the proposed framework is also improved by three novel procedures: (1) a simplified calibration method is proposed and added for the threshold selection process; (2) a noise blob filter is proposed to get rid of the noise blobs. (3) a boundary smoothing filter is proposed to reduce the false seeds produced by the iterative erosion. As it turned out, the quantification accuracy of the proposed framework increases from 93.4 to 96.8% compared to the previous method. In addition, the accuracy of the proposed framework is also better in quantifying the muscle cells than two available state-of-the-art methods.

Conclusions

The proposed framework is able to automatically segment and quantify more types of cells than state-of-the-art methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1604-1) contains supplementary material, which is available to authorized users.

Segmentation of digitized histological sections for quantification of the muscularized vasculature in the mouse hind limb

Immunohistochemical tissue staining enhances microvasculature characteristics, including the smooth muscle in the medial layer of the vessel walls that is responsible for regulation of blood flow. The vasculature can be imaged in a comprehensive fashion using whole-slide scanning. However, since each such image potentially contains hundreds of small vessels, manual vessel delineation and quantification is not practically feasible. In this work, we present a fully automatic segmentation and vasculature quantification algorithm for whole-slide images. We evaluated its performance on tissue samples drawn from the hind limbs of wild-type mice, stained for smooth muscle using 3,3'-Diaminobenzidine (DAB) immunostain. The algorithm was designed to be robust to vessel fragmentation due to staining irregularity, and artefactual staining of nonvessel objects. Colour deconvolution was used to isolate the DAB stain for detection of vessel wall fragments. Complete vessels were reconstructed from the fragments by joining endpoints of topological skeletons. Automatic measures of vessel density, perimeter, wall area and local wall thickness were taken. The segmentation algorithm was validated against manual measures, resulting in a Dice similarity coefficient of 89%. The relationships observed between these measures were as expected from a biological standpoint, providing further reinforcement of the accuracy of this system. This system provides a fully automated and accurate means of measuring the arteriolar and venular morphology of vascular smooth muscle.

Automated high-content morphological analysis of muscle fiber histology

In the search for a cure for many muscular disorders it is often necessary to analyze muscle fibers under a microscope. For this morphological analysis, we developed an image processing approach to automatically analyze and quantify muscle fiber images so as to replace today's less accurate and time-consuming manual method. Muscular disorders, that include cardiomyopathy, muscular dystrophies, and diseases of nerves that affect muscles such as neuropathy and myasthenia gravis, affect a large percentage of the population and, therefore, are an area of active research for new treatments. In research, the morphological features of muscle fibers play an important role as they are often used as biomarkers to evaluate the progress of underlying diseases and the effects of potential treatments. Such analysis involves assessing histopathological changes of muscle fibers as indicators for disease severity and also as a criterion in evaluating whether or not potential treatments work. However, quantifying morphological features is time-consuming, as it is usually performed manually, and error-prone. To replace this standard method, we developed an image processing approach to automatically detect and measure the cross-sections of muscle fibers observed under microscopy that produces faster and more objective results. As such, it is well-suited to processing the large number of muscle fiber images acquired in typical experiments, such as those from studies with pre-clinical models that often create many images. Tests on real images showed that the approach can segment and detect muscle fiber membranes and extract morphological features from highly complex images to generate quantitative results that are readily available for statistical analysis.

Grain-oriented segmentation of images of porous structures using ray casting and curvature energy minimization

We segment an image of a porous structure by successively identifying individual grains, using a process that requires no manual initialization. Adaptive thresholding is used to extract an incomplete edge map from the image. Then, seed points are created on a rectangular grid. Rays are cast from each point to identify the local grain. The grain with the best shape is selected by energy minimization, and the grain is used to update the edge map. This is repeated until all the grains have been recognized. Tests on scanning electron microscope images of titanium oxide and aluminium oxide show that their process achieves better results than five other contour detection techniques.

SMASH - semi-automatic muscle analysis using segmentation of histology: a MATLAB application

Background

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.

Results

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.

Conclusions

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.