Skip to main content
2024 BMES Annual Meeting Main banner
Icon Legend
This session is not in your schedule.
This session is in your schedule. Click again to remove it.
Back

Biomedical Imaging and Instrumentation

Biomedical Imaging and Instrumentation - Poster Session E

Poster R12 - Novel Automated Mature Myofiber Segmentation to Improve Image Analysis of Regenerating Myofibers

Saturday, October 26, 2024
10:00 AM – 11:00 AM EST
Location: Exhibit Hall E, F & G

Introduction:  Myofiber maturity is a hallmark of skeletal muscle regeneration after injury. Mature myofibers—indicated by peripherally located nuclei— are the primary cell state present in uninjured muscle. However, following injury, the repair process of myogenesis is initiated and new myofibers regenerate into mature myofibers to replace damaged tissue. As immature myofibers have centrally located nuclei, the rate of muscle regeneration can be quantified by measuring the number of myofibers with peripheral versus central nuclei. Despite the importance of analyzing muscle regrowth via myofiber maturity, there are currently no open-source options for automated quantification of mature and immature myofibers. Generally, manual cell segmentation methods are time intensive and prone to human error. To reduce the time burden and inconsistency associated with manual myofiber segmentation, we developed a novel ImageJ plugin Mature Myofiber Segmenter (MaMyS) to automatically identify and quantify mature and immature myofibers. This code identifies cells with centrally located nuclei and highlights the regenerating myofibers. MaMyS consists of three sections: (1) Image processing (2) ROI modification, and (3) Myofiber segmentation/ quantification. MaMyS provides a low-cost, user-friendly, and modifiable framework that reduces image analysis time thus alleviating time constraints associated with manual myofiber segmentation. Automated image analysis of muscle regeneration using MaMyS will increase the consistency of data across varying user experience levels and allow for the analysis of more images, all of which improve the quality of information collected.

Materials and

Methods:  Cross-sectional images of the tibialis anterior from male C57BL/6J mice collected 2-, 7-, and 21-days post-injury were utilized to develop MaMyS. Muscle tissue was immunofluorescently stained with Laminin to delineate myofiber boundaries in red. The tissues were then mounted with Vectashield + DAPI, staining the nuclei blue. Images were captured on a Zeiss LSM 900 confocal microscope at 20X. The deep-learning based algorithm Cellpose was used to create masks outlining cell boundaries, made into regions of interest (ROIs) via the ImageJ plugin LabeltoROIs (Figure 1a,e). Images and their ROIs were fed into our novel program MaMyS for analysis, taking them through three sections. (1) Image processing was done in MaMyS to remove background noise by selecting for the blue channel and applying grey morphology > Gaussian blur > Auto threshold, shown in (Figure 1i-l). (2) Next, the ROIs outlining the myofibers were quantified then resized to exclude peripheral nuclei. (3) Finally, the program sorted the ROIs into sets of mature and immature myofibers based on an adjustable threshold of pixels (Figure 1d,h). The sorted myofibers were then recorded into the results table (Figure 1o). MaMyS was developed using IntelliJ and the Mauve (Apache) build tool. To quantify how MaMyS improved accuracy compared to manual image analysis, the time required for manual and MaMyS processing of a high-quality image (Photo 1) and a low-quality image (Photo 2) were determined. Significance was analyzed via One-Way ANOVA in GraphPad Prism, with p < 0.05 defined as significant and an n=3 for each group.

Results, Conclusions, and Discussions: Images analyzed with MaMyS required less time and resulted in less variation in the cross-sectional area compared to manual counting. Photo 1 contained 682 myofibers and was run with MaMyS alone, labeled as “MaMyS”. The program took approximately 42.2 seconds to run and had no significant variance in % area of immature myofibers between both the subsequently manually checked runs “Auto” and the hand segmented runs “Manual”. The “Auto” and “Manual” run took approximately 231.2 and 551.5 seconds respectively. The time taken for Photo 2 which contained 1127 myofibers was 70.1, 944.8, and 1758.0 seconds for “MaMyS”, “Auto”, and “Manual” respectively. However, runs that used MaMyS resulted in a consistently lower % area of immature myofibers with a difference of -0.76% and -12.16% for photo 1 and 2 between automatic and manual quantification methods.

The field of regenerative medicine requires high throughput quantification of mature and immature myofibers to accurately assess the regeneration rate of muscle. However, methods available are limited to slow manual sorting, or expensive software. Our novel program MaMyS alleviates this time constraint and minimizes user bias. MaMyS is promising, providing a similar % area for immature myofiber to manual methods for a fraction of the effort and reducing the time required for quantification by 92.3%. However, some modifications are still in development. The image processing steps remove background noise, resulting in a 0.3% false positive rate, but fainter signals are excluded from the auto-threshold. This leads to a trend underestimating the % area of the immature myofibers. The image processing will be modified to allow users to adjust their settings, with default values for high- and low-quality images. In addition, future iterative design will improve accuracy by basing the segmentation threshold on both % area and pixel count to better account for small myofibers. Reliable identification of the maturation state of myofibers allows researchers to quantify the progression of skeletal muscle myogenesis after an injury. This tool provides a time-effective, modifiable method of segmenting which cells, decreasing the time required to carry out this analysis, while increasing consistency, and improve data rigor and workflow.

Acknowledgements (Optional):