# MyoFInDer: An AI-Based Tool for Myotube Fusion Index Determination

Antoine Weisrock, Rebecca Wüst, Maria Olenic, Pauline Lecomte-Grosbras, Lieven Thorrez

► To cite this version:

Antoine Weisrock, Rebecca Wüst, Maria Olenic, Pauline Lecomte-Grosbras, Lieven Thorrez. MyoFInDer: An AI-Based Tool for Myotube Fusion Index Determination. Tissue Engineering: Parts A, B, and C, 2024, 30 (19-20), pp.652-661. 10.1089/ten.TEA.2024.0049. hal-05515151

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# MyoFInDer: An AI-Based Tool for Myotube Fusion Index Determination

Antoine Weisrock<sup>a,b</sup>, Rebecca Wüst<sup>b</sup>, Maria Olenic<sup>b</sup>, Pauline Lecomte-Grosbras<sup>a</sup>, and Lieven Thorrez<sup>b</sup>

<sup>a</sup>Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multi-échelle, F-59000 Lille, France

<sup>b</sup>Tissue Engineering Lab, Department of Development and Regeneration, KU Leuven, E. Sabbelaan 53, 8500 Kortrijk, Belgium

## Abstract

The fusion index is a key indicator for quantifying the differentiation of a myoblast population, which is often calculated manually. In addition to being time-consuming, manual quantification is also error-prone and subjective. Several software tools have been proposed for addressing these limitations, but suffer from various drawbacks including unintuitive interfaces and limited performance. Here, we describe MyoFInDer, a Python-based program for the automated computation of the fusion index of skeletal muscle. At the core of MyoFInDer is a powerful artificial intelligence-based image segmentation model. MyoFInDer also determines the total nuclei count and the percentage of stained area, and allows for manual verification and correction. MyoFInDer can reliably determine the fusion index, with a high correlation to manual counting. Compared to other tools, MyoFInDer stands out as it minimizes the inter-operator variability, minimizes process time and displays the best correlation to manual counting. Therefore, it is a suitable choice for calculating fusion index in an automated way, and gives researchers access to the high performance and flexibility of a modern artificial intelligence model. As a free and open-source project, MyoFInDer can be modified or extended to meet specific needs.

## Impact statement

We present an efficient and user-friendly image analysis tool, MyoFInDer, to quantify myotube fusion index and the total number of nuclei in an automated and reliable way. MyoFInDer uses fluorescence microscopy images from both 2D cell cultures and 3D tissue cultures. It is of direct use for muscle tissue engineering and can be adapted in other fields. The software combines the best of both AI-based automated image analysis and manual correction. Use of MyoFInDer saves users time and energy compared to manual quantification, and lowers the operator-bias within research teams.

## 1 Introduction

In skeletal muscle culture, differentiation is a key step in which mono-nucleated myoblasts fuse to form multi-nucleated myofibers. The fusion index is an indicator used for quantifying the level of differentiation in a myoblast culture. Along with other myogenic parameters, it provides information on myogenic capacity of stem cells and can be used to assess the influence of drugs or external stimuli on muscle development [1], [2], [3]. The fusion index is calculated as the ratio of nuclei within differentiated myotubes over the total number of myoblast nuclei in the culture.

In practice, the fusion index is often determined based on microscopy images of immunofluorescence-stained samples, for example using DAPI and tropomyosin for identifying the nuclei and the myofibers
respectively. It is still common to manually count the nuclei when calculating fusion indexes [4], [5], [6]. This has the advantage of being easy to implement, and researchers can handle image variability much better than a program. However, a single image can contain thousands of nuclei, and several images are required to ensure an accurate measure of the fusion index. Manual counting is, therefore, a tedious activity. Furthermore, this method is prone to operator bias and does not guarantee good reproducibility when conducted by different researchers.

In an effort to speed up the calculation and minimize possible human biases, three automated solutions have been developed for fusion index calculation and published. The MATLAB-based MyoCount executable was the first software tool to automate the computation of fusion index and the myotube surface area [7]. It was followed by the ViaFuse macro written in Fiji [8], which was described to perform better on the fusion index calculation and can also compute the cell viability [9]. The last released software is The Myotube Analyzer, also written in MATLAB, and capable of assessing a much more comprehensive set of myogenic characteristics including the fusion index [10]. In addition to these three published solutions, it may also be possible to write automated pipelines for fusion index calculation in general-purpose image-analysis interfaces like Icy [11] or CellProfiler [12], [13].

Artificial Intelligence (AI) algorithms for image processing have made fast progress in recent years, and access to these algorithms beyond the programming community has improved. For the specific application of cell image segmentation, numerous AI-based programs have already been released [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]. While conventional algorithms require recalibration of parameters to adapt to new types of images, changes in acquisition settings, experimental variation etc., these AI models demonstrate much greater adaptability and similar or superior performance [24]. Convenient interfaces for researchers to train models on their own datasets are now available and require very limited AI expertise [19], [20]. They still necessitate researchers to annotate their data, which is often done manually and represents a tedious activity. Other models are trained on large datasets of cell images and are thus able to adapt to numerous types of images used in biomedical research from the start [18], [21], [22], [23]. These pre-trained generalist models are particularly interesting for building image segmentation interfaces, as they are directly runnable and perform well on a wide variety of images. Many of them are also easily accessible through plugins in ImageJ [25], web interfaces, or easy to implement in popular programming languages such as Python [26] or Java [27]. In addition, these models are still evolving rapidly and will most likely continue to improve in the near future.

Here we introduce MyoFInDer (Myotube Fusion Index Determination software), a free and open-source AI-based Python module for the automatic fusion index calculation on microscopy images. It provides a user-friendly interface designed for high-throughput analysis and relies on the existing cell image segmentation model Mesmer [23] for calculations. Compared to similar existing software solutions (MyoCount, ViaFuse, The Myotube Analyzer), MyoFInDer is (i) running in Python rather than Fiji or Matlab (ii) based on AI rather than classic computer vision (iii) focusing on the fusion index (iv) high-throughput oriented. MyoFInDer is also able to evaluate the total nuclei count and the fraction area of stained myotubes, although its primary function is the fusion index calculation. The module and its usage are briefly described in this article, and detailed documentation along with installation instructions can be found online at https://tissueengineeringlab.github.io/MyoFInDer/. In the following sections, we compare the performance of MyoFInDer with manual counting and the existing solutions over a set of representative images, and demonstrate that MyoFInDer performs better in terms of computation speed, inter-operator dispersion of the results, and fidelity to manual counting. We also present an experimental application of MyoFInDer for evaluating bovine myoblast differentiation capacity after expansion in medium containing p38 MAP kinase inhibitor. Then, we discuss the evaluated solutions, their interfaces, and their respective limitations.
# 2 Method

## 2.1 Myoblast fusion assay and histological staining

The benchmarking of MyoFInDer was performed using microscopy images of myoblasts fused into myotubes. Human myoblasts were obtained from human vastus lateralis muscle and extraocular muscle, isolated as described in [28]. Bovine myoblasts were isolated from biceps femoris of fetal Belgian Blue cattle. Murine myoblasts were obtained from the American Type Culture Collection (ATCC). For determining the fusion capacity of the cells, a fusion assay was performed in a 24-well plate, following the protocol described in [4]. Formed myotubes were chemically fixed using 4% paraformaldehyde and methanol and stained using immunofluorescence. First, myotubes were labeled with primary antibodies against tropomyosin (T9283, Invitrogen). Second, an Alexa Fluor 488-labeled anti-human secondary antibody (A-11029, Invitrogen) was added and nuclei were stained with DAPI (1:10,000, Thermo Fisher). Fluorescent images were taken using an inverted light microscope (Axio Vert.A1) using a 10× magnification. Table 1 summarizes the different culture types used in the present study, the staining used, and the settings for microscopy image acquisition.

<table><caption>Table 1: Overview of the staining and image acquisition parameters for the different culture types</caption><thead><tr><th></th><th>Cow cells</th><th>Human cells<br>(extraocular)</th><th>Human cells<br>(vastus lateralis)</th><th>Murine cells</th></tr></thead><tbody><tr><td>Representative<br>Image of<br>Myotubes</td><td></td><td></td><td></td><td></td></tr><tr><td>Scaling (per<br>Pixel)</td><td>0.454 µm ×<br>0.454 µm</td><td>0.454 µm ×<br>0.454 µm</td><td>0.908 µm ×<br>0.908 µm</td><td>0.454 µm ×<br>0.454 µm</td></tr><tr><td>Image Size<br>(Pixel)</td><td>1936 × 1460</td><td>1936 × 1460</td><td>968 × 728</td><td>1936 × 1460</td></tr><tr><td>Bit Depth</td><td>14 bit</td><td>14 bit</td><td>14 bit</td><td>14 bit</td></tr><tr><td>Objective</td><td>10 ×</td><td>10 ×</td><td>10 ×</td><td>10 ×</td></tr><tr><td>Immuno-<br>fluorescence<br>Staining</td><td>Alexa Fluor 488<br>(green), DAPI<br>(blue)</td><td>Alexa Fluor 488<br>(green), DAPI<br>(blue)</td><td>Alexa Fluor 488<br>(green), DAPI<br>(blue)</td><td>Alexa Fluor 488<br>(green), DAPI<br>(blue)</td></tr><tr><td>Fusion Index</td><td>(37 ± 1) %</td><td>(66 ± 11) %</td><td>(71 ± 1) %</td><td>(28 ± 5) %</td></tr></tbody></table>

## 2.2 Manual characterization of the fusion index

Manual characterization of the fusion index was performed using the FIJI software [8]. First, images were loaded, and the different color channels were split. Next, the empty red channel was discarded and the Look Up Table (LUT) of the blue channel was inverted. The blue channel was then combined with the green channel to a new stack and the two layers of the stack were merged into a single image as a Z-projection. Finally, the Point tool was used to pinpoint positive (nuclei within a tropomyosin positive area) and negative (nuclei outside a tropomyosin-stained area) with separate counters. The displayed number of positive and negative nuclei was finally read from the interface. For determining the execution time, a timer was started before starting up FIJI and stopped after the last nucleus was pinpointed.

## 2.3 Software characterization of the fusion index

The fusion index calculation in MyoCount was performed following the guidelines described in [7]. First, the original microscopy images were exported to .tif format from the Zeiss Zen Blue software. Then, for each image, the program was started from the command-line interface, so that parameters could be
provided. Finally, the computed fusion index was retrieved from the generated results file. A timer was started right before launching the command, and stopped right after the results file was written by the program.

In The Myotube Analyzer, only a part of the functionalities described in [10] was used when computing the fusion index. First, the blue and green channels of the original microscopy images were exported to separate .png files from the Zeiss Zen Blue software. For each image, these two images were then imported in the graphical interface of the program, and their intensity thresholds were adjusted. Next, the fiber mask was adjusted in the dedicated menu, by setting its threshold and de-noising. No manual correction of the fiber mask was performed. The px/mm ratio of the current image was then provided, and the fusion index calculation was started. No manual correction was applied to the obtained result, and the final fusion index and number of detected nuclei could be read directly from the interface. A timer was started right before opening the interface, and stopped when the figures of interest were displayed in the interface.

In MyoFInDer, the following steps were taken to compute the fusion index. First, the original microscopy images were exported to .jpg format from the Zeiss Zen Blue software. Images of the same culture type were then loaded in the interface together, and the available computation settings were adjusted in the dedicated menu. The computation was then started on the entire batch of images, and the number of detected nuclei and the fusion index for each image could be read from the interface. In a second stage, the output was manually corrected for each image, and the corrected nuclei count and fusion index were again read from the interface. A timer was started right before opening the interface, and stopped when the computation ended. For each image, a new timer was then started for timing the manual correction.

In MyoFInDer, the time necessary to process an image was determined as the total time necessary to compute all images of a given culture type at once, divided by the number of processed images. To calculate the operation time in MyoFInDer with manual correction, the time necessary to correct the image was added to the operation time determined for the same image with MyoFInDer without manual correction. A more detailed description of MyoFInDer's usage and options is available in Supplementary Method SM1, as well as in the online documentation. The fiber and nuclei detection steps in MyoFInDer's interface are illustrated in Figure 1. The detail of the internal steps performed by MyoFInDer when processing an image can be found in Figure 2. The nuclei and fiber channels are handled differently, as only classical image processing operations are used for fiber detection, whereas AI-based segmentation is used for nuclei detection. Specifically, the Mesmer AI model is used for segmentation. It is a pre-trained neural network specialized in whole-cell and nuclear segmentation of microscopy images of cell cultures. The behavior of the whole algorithm can be fine-tuned in multiple ways through settings accessible to the user in the interface. This way, users can adapt the pipeline and compensate for variability in the staining or microscopy settings between experiments.

# 3 Experiment

## 3.1 Experimental Design

To verify the accuracy of MyoFInDer's output compared to manual counting, two studies were conducted. First, primary fetal bovine myoblasts were expanded for up to 3 passages under two different culture conditions, and a fusion assay was then performed to determine whether their differentiation capacity differed between the two conditions. The conditions included a treatment group, where the expansion medium contained 10 µmol p38 MAP kinase inhibitor (Merck, SML0543), and a control group where no inhibitor was added. During the fusion assay, 10 images were acquired from each condition as described in Section 2.1. For each image, the fusion index was determined by three independent operators, using the manual counting method in FIJI and using MyoFInDer (without manual correction). For each operator, an unpaired t-test was performed to characterize the difference of fusion index between the two conditions. The result obtained with MyoFInDer was then compared to that of manual counting to test the accuracy of the software.
<figure><img src="image_2.png" /><figcaption>Figure 1: Illustration of the processing steps taken by MyoFInDer. First, the fibers are detected and outlined. Then, the nuclei are detected, categorized, and pinpointed. Finally, these two inputs are combined. The four included images were obtained by adjusting the visualization parameters in the interface, and serve only as an illustration of the internal computation steps.</figcaption></figure>

A second study was conducted to compare the performance of MyoFInDer with manual counting and other available programs. To this end, we extended our data set to include images of myotubes from different species and different muscle types. Fluorescence-stained images of these myoblasts fused into myotubes were acquired as described in Section 2.1. The additional set consisted of 5 images of murine myoblasts, 5 images of human myoblasts isolated from an extra-ocular muscle, and 5 images of human myoblasts isolated from a vastus lateralis muscle. These three extra culture types were chosen to represent the diversity of myoblast cultures from different origins, and therefore to test the programs on a variety of images.

For each image, three different parameters were acquired: the fusion index, the total number of nuclei, and the execution time. They were determined using (i) the manual counting method in FIJI, (ii) the MyoCount software, (iii) The Myotube Analyzer software, (iv) MyoFInDer and (v) MyoFInDer with manual correction. For images of each culture type, operators were free to adjust the available software parameters to the values they deemed most appropriate. This decision was taken in order to mimic closely the real-life use-cases of the programs.

All programs were evaluated and analyzed by three independent operators. The raw data gathered from all three operators is available in [29]. The raw dataset contains 1575 values: 3 operators, 4 culture types, 1 culture type with 20 images and 3 culture types with 5 images, 5 methods, 3 extracted parameters.

As a first step, for each operator, counting method, culture type, and parameter, the values obtained for all images of a given culture type were averaged into a single number. Only *n* = 180 values were therefore remaining. From these averaged values, two additional indicators were derived. First, for each program, the square of the Pearson correlation coefficient between the software-calculated values and the manually obtained ones was computed (all culture types and investigators combined). This coefficient was calculated for both the fusion index and the total nuclei count (*n* = 10 coefficient values). Then,
<figure><img src="image_3.png" /><figcaption>Figure 2: Flowchart of the different processing steps applied in MyoFInDer when performing a fusion index calculation.</figcaption></figure>

for each program and culture type, the inter-operator standard deviation of the fusion index and total nuclei count was computed ($n = 40$ standard deviation values). Each standard deviation value was then divided by the standard deviation of the manual counting method, performed by the same operator on images of the same culture type. As a result, numbers quantifying the relative increase or decrease in inter-operator deviation compared to manual counting were obtained ($n = 40$ values).

## 3.2 Experimental Results

First, the validity of MyoFInDer's output in the context of an experimental application was assessed. For this experiment, bovine myoblasts were expanded ($n = 10$) for up to 3 passages in medium containing 10 µmol p38 MAPK kinase inhibitor (treatment group), differentiation was induced, and the fusion index was measured. Myoblasts expanded in medium without the addition of p38 MAP kinase inhibitor were used as a control (Figure 3). The treatment group displayed a higher fusion index than the control group, and the difference was significant (unpaired t-test, $p \le 0.002$) regardless of the operator or the counting method. Manual image counting from 3 operators showed that the difference between groups was (32.33 ± 1.65)%. MyoFInDer used by the 3 operators without corrections showed a group difference of (29.93 ± 0.32)%. The inter-operator variability was smaller when MyoFInDer was used, while the result was very similar, making the software both objective and accurate for fusion index comparisons.

<figure><img src="image_4.png" /><figcaption>Figure 3: Application of MyoFInDer to evaluate fusion index of bovine myoblasts treated with p38 MAP kinase inhibitor during the expansion phase. MyoFInDer fusion index calculation proved similar to manual counting and helped to identify significant difference ($p \le 0.002$) between treatment and control groups.</figcaption></figure>

Then, based on the data gathered from an extended set of images, several values were derived to quantitatively compare the evaluated fusion index determination methods. First, Pearson correlation
coefficients between the results obtained from manual counting and those obtained from software counting using MyoFInDer, MyoCount, and The Myotube Analyzer were computed (Figure 4). For the fusion index calculation, MyoFInDer correlates better with the manual counting ($R^2 = 0.91$) than the two other programs ($R^2 = 0.75$ and $0.86$) (Figure 4A). This means that fusion index variations observed between different images with manual counting are more reliably reflected in the results obtained from MyoFInDer. In Figure 4B, a high degree of correlation between the manual- and software-determined nuclei count can be observed. Also, for this parameter, MyoFInDer exhibits a higher correlation coefficient ($R^2 = 0.97$) than the other programs ($R^2 = 0.89$ and $0.94$), and can therefore be considered more faithful to manual counting.

<figure><img src="image_5.png" /><figcaption>Figure 4: Correlation between the output of software counting methods and that of manual counting. A) Software-determined fusion index values compared to manually-determined ones, for MyoFInDer, MyoCount, and The Myotube Analyzer. B) Software-determined total numbers of nuclei compared to manually-determined ones for MyoFInDer, MyoCount, and The Myotube Analyzer. On each graph, the square of the Pearson correlation coefficient is indicated.</figcaption></figure>

The inter-operator standard deviation of the results obtained for each culture type was computed as described in Section 3.1, and plotted for MyoFInDer, MyoCount, and The Myotube Analyzer (Figure 5). For the total nuclei count, Figure 5A indicates that MyoFInDer allows to match or reduce the inter-operator variability compared to manual counting for all four culture types. This is only the case for two culture types with The Myotube Analyzer and MyoCount. The highest dispersion achieved using MyoFInDer is also considerably lower than those of the other programs. Regarding the standard deviation of the fusion index values, all three solutions lead to an overall decrease in the inter-operator variability compared to manual counting (Figure 5B). However, MyoFInDer stands out, as the decrease in variability it brings is both important and consistent for all four culture types. It can be concluded that using MyoFInDer allows to decrease the inter-operator variability observed with manual counting, and to a globally higher extent than with the other tested programs.

Next, the execution time needed to process an image for each culture type and counting method was compared (Figure 6). The results clearly show that all the software solutions require less time to analyze the images than manual counting. For all four culture types, MyoFInDer runs faster than the manual counting and also faster than the other tested programs, and its execution time is consistent for
<figure><img src="image_6.png" /></figure>

Figure 5: Inter-operator standard deviation for MyoFInDer, MyoCount, The Myotube Analyzer, and manual counting, with manual counting taken as a reference. A) Standard deviation for the total number of nuclei. B) Standard deviations for the fusion index. Individual data points represent the inter-operator standard deviation for a given culture type.

all the culture types (Figure 6). Therefore, MyoFInDer appears to be the solution that saves the most execution time compared to manual counting.

<figure><img src="image_7.png" /></figure>

Figure 6: Operation time per image for MyoFInDer, MyoCount, The Myotube Analyzer, and manual counting, for the four tested culture types.

In addition, the same parameters as in previous results were compared between MyoFInDer and MyoFInDer with manual correction (Figure 7). Results indicate that performing manual correction in MyoFInDer brings a negligible improvement to the degree of correlation with manual counting (Figure 7A, B). However, comparing the inter-operator standard deviation suggests that performing the manual correction has a beneficial impact on the inter-investigator dispersion (Figure 7C). Lastly, Figure 7D indicates that performing a manual correction considerably increases the execution time for each image.

In this study, and in the existing works describing fusion index determination programs, the manual
counting method is taken as a reference. This method is, however, known to be error-prone, and subject to operator bias. Using Bland-Altman plots (Supplementary Figure SF1), we could identify fixed inter-operator biases of up to 10 percentage points for the manual fusion index calculation, and a few images for which the inter-operator difference lies beyond the 95% limits of agreement. It is clear from Supplementary Figure SF1 that inter-operator bias is greatly reduced when using MyoFInDer.

# 4 Discussion

The new MyoFInDer Python module allows for calculation of myoblast fusion indices, based on microscopy images, using an existing AI model for image segmentation [23]. It is the first AI-based software specifically designed for automating the fusion index determination. Its implementation was briefly described, an experimental application was presented, and its performance was compared to that of other existing solutions. Overall, a good accordance was found between the results obtained with MyoFInDer and those obtained with manual counting. The software accurately computed the fusion index on images of myotubes from different species and muscle types (Table 1), all of which displayed different morphologies. Moreover, compared to both manual counting and the other software solutions, using MyoFInDer resulted in a lower inter-operator variability and greatly reduced the total operation time. MyoFInDer is, therefore, an alternative to the existing solutions for fusion index determination.

The ViaFuse macro [9] in FIJI, that was planned to be included in the software comparison experiment, could ultimately not be used due to a bug at runtime. We reported this bug to the maintainers of the program, as did other users before us, but could not get any feedback. Therefore, the ViaFuse macro was not evaluated in this study.

In the present study, we have only considered published software tools. However, some general-purpose image-processing programs like Fiji [8], Icy [11] or CellProfiler [12], [13] allow building advanced pipelines with a limited knowledge in computer-vision techniques. It is therefore likely that some laboratories use custom-developed but unpublished solutions for automating the fusion index determination. For MyoFInDer, Python was preferred to the available general-purpose interfaces, as it provides a much richer environment for building a tailor-made user interface.

Pearson correlation coefficients between manual and software counting were determined. Similar results can be found in the articles introducing MyoCount [7] and ViaFuse [9], specifically comparing MyoCount to manual counting. For the total nuclei count, Hinkle et al. found a correlation coefficient of 0.94, identical to the value that we determined. For the fusion index, both Murphy et al. and Hinkle et al. found a value of 0.87, again similar to the coefficient that we obtained (0.86). The good agreement between the previous results and our new values suggests that we implemented the existing tools as designed and also confirms that our datasets were representative.

Besides the measurable parameters for comparing the performances of the counting methods presented in the previous section, other non-quantifiable key differences need to be outlined. They are summarized in Table 2. MyoFInDer implements several features aiming to facilitate image processing for the operator, which not all other programs possess. In particular, the possibility to process images in batches saves users the trouble of having to start each computation individually, which significantly reduces the hands-on time investment, as the whole image set can be processed in the background. Also, the possibility of saving a project can be of major interest for sharing it with other team members or re-opening it and continuing the analysis later.
<figure><img src="image_8.png" /><figcaption>Figure 7: Comparison between the output of MyoFInDer with and without manual correction. A) Correlation between the software-determined fusion index and that of manual counting. B) Correlation between the software-determined total number of nuclei and that of manual counting. C) Inter-operator standard deviation, for the total number of nuclei and the fusion index. D) Operation time per image for the four tested culture types.</figcaption></figure>
<table><caption>Table 2: Overview comparison of qualitative parameters for different software solutions.</caption><thead><tr><th></th><th>Possibility to process batches of images</th><th>Supported image formats</th><th>Requirements</th><th>Parameter adjustment</th><th>Possibility to export results</th><th>Possibility to save a project</th><th>Supported operating systems</th></tr></thead><tbody><tr><td>MyoFInDer</td><td>Yes</td><td>.tif, .png, .jpeg, .bmp</td><td><ul><li>Python 3.7 to 3.10</li><li>Microsoft C++ build tools</li></ul></td><td>In the graphical interface</td><td>Yes</td><td>Yes</td><td><ul><li>Windows</li><li>Linux</li><li>macOS</li></ul></td></tr><tr><td>MyoCount</td><td>Yes</td><td>.tif</td><td><ul><li>Matlab Runtime R2018a</li></ul></td><td>In the command-line interface</td><td>Yes</td><td>No</td><td><ul><li>Windows</li></ul></td></tr><tr><td>The Myotube Analyzer</td><td>No</td><td>.png</td><td><ul><li>Matlab Runtime R2021a</li></ul></td><td>In the graphical interface</td><td>No</td><td>No</td><td><ul><li>Windows</li></ul></td></tr></tbody></table>
One of the advantages of MyoFInDer over MyoCount and ViaFuse is the possibility to manually correct the output of the computation. We showed that manual correction has a limited yet beneficial impact on the inter-operator deviation, but significantly increases the operation time. A possible cause for the little improvement offered by the manual correction in our study is the good quality of the processing performed by MyoFInDer. This quality is, however, also strongly dependent on the quality of the processed images. Defects in the images that may have a detrimental influence on the computation output include: a poor contrast, spurious background signal, the presence of out of focus objects in the background and signal overlap between channels. Manual correction can, in such cases, have a more substantial influence on the final output.

Potential causes for the identified inter-operator biases include: a different categorization of nuclei in non-trivial situations, different exclusion criteria for small stained particles, variable care and time dedicated to the manual counting task, and many more. The identified biases were not artificially corrected in our study. We demonstrated that inter-operator bias is greatly reduced when using MyoFInDer.

MyoFInDer was originally designed for processing microscopy images of fluorescence-stained myoblast cultures. However, beyond the specific application of nuclei segmentation, the Mesmer AI library used for computation performs well on various types of microscopy images used in biomedical research. MyoFInDer interface is therefore not limited to the calculation of the fusion index: it is suitable for any application requiring to detect round or ovoidal shapes over a partly stained background. It was for instance successfully used in our group for assessing the purity of cell populations, quantifying the expression of specific markers within a cell population or quantifying cell viability. MyoFInDer is thus a practical tool for automating or speeding-up manual counting tasks, even beyond its original purpose of determining the fusion index.

Although MyoFInDer was found to perform well, it shows some limitations due to the development choices that were made. The interface was purposely kept simple, in order to limit the possible subjective choices for the users, and to minimize the time needed to process images. As a consequence, MyoFInDer lacks the fine-tuning options that are present in The Myotube Analyzer, especially for fiber segmentation. Nevertheless, users can easily compensate for any inaccuracy via manual correction. Compared to the other solutions which can all calculate at least two myogenic parameters, MyoFInDer's scope is also more restricted since it was primarily developed for fusion index analysis. Again, a trade-off was made between the simplicity of the interface and the options it offers.

# 5 Conclusion

In the present article, we introduced MyoFInDer, a Python module for automated quantification of the fusion index on microscopy images of fluorescence-stained myotubes. Unlike the existing software solutions, it relies on an AI model for detecting cell nuclei. It consists of a user-friendly interface that necessitates little input from the user, and performs the analysis in a quick and reliable manner. Its output was found to be strongly correlated to that of manual counting and of similar programs, confirming its accuracy. Compared to the available published methods, MyoFInDer also stands out as the one that minimizes both the inter-operator deviation, and the operation time. In addition, the possibility to manually correct the output offers a powerful way to combine the speed of an automated tool with the flexibility of manual counting. In this study, MyoFInDer was demonstrated as the optimal solution for computing fusion indexes among the current existing programs. MyoFInDer was designed and released as a free and open-source project and can be further modified by other contributors.
# Supplementary Material

## Supplementary Method SM1: MyoFInDer usage

MyoFInDer consists mainly of a graphical interface. In this interface, the normal operation is as follows. First, users should load the images they want to process using the Load Images button. A popup window appears in which users can interactively select the images to load. Once the images are loaded, they are listed in the information frame on the right of the interface. On the left of the interface, the currently selected image is displayed. Using their mouse wheel, users can zoom in and out of this image and drag it. To change the displayed image, users can click on another image in the information frame. Before processing the images, users can adjust several settings in the Settings menu. The available settings include the minimum diameter of the nuclei to detect, the color channels for the nuclei and fibers, etc. Once the settings are adjusted, users can start a computation by clicking on the Process Images button. It is possible to start a computation for all or only part of the loaded images, depending on the checkboxes ticked in the information frame.

Once a computation is over, an overlay is added to the processed images and allows to visualize the computed results. The detected nuclei are pinpointed by color circles: one color for negative nuclei, and another color for positive nuclei. The detected fibers are outlined by a continuous color line. It is possible to hide the overlays and/or certain color channels using checkboxes located at the top-right corner of the interface. Numeric information on the results is displayed in the information frame for each computed image: the total number of nuclei, the number of positive nuclei, the fusion index, and the fractional area of myotubes. Using the mouse buttons, users can manually correct the output of the computation. The left button is for adding a new nucleus or switching an existing nucleus from positive to negative or vice-versa. The right button is for deleting an existing nucleus. Once users have manually corrected their images, they can save their results as a project by clicking on the Save As button. They will be able to choose the location for their project in a dedicated window. A saved project contains: the original images, data on the detected fibers and nuclei, the last computation settings used, and a results file in the Excel format. Projects can be shared between users and re-opened later using the Load Project menu entry.
<figure><img src="image_9.png" /><figcaption>Supplementary Figure SF1: Operator Bias</figcaption></figure>

Figure 8: Bland-Altman plots for pair-wise comparison of the operators, with manual counting and MyoFInDer. Densely dotted lines represent the 95% limits of agreement, and dotted lines represent the fixed bias.
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