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IMAGEJ as a Tool to Quantify Events after Spinal Cord Injury

Ooi Mei Xuan
4th Year Biomedicine Course
Universiti Sains Malaysia

With the advancement of artificial intelligence and digitalism, biomedical image processing and analysis are widely used by researchers nowadays to obtain results in large numbers rapidly and accurately. Examples of biomedical image analysis that are commonly used in the medical field are Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). ImageJ is a free software used for biomedical image processing and analysis. It is accessible, flexible to experimental research design, and is a platform to implement image processing algorithms. By using ImageJ, one can process and analyze images using its functions as well as acquiring the extension support from macros and plugins.

ImageJ can measure the confluency of a cell which is performed by the PHANTAST plugin in ImageJ. Quantification of confluency analyzes the density of cells of a culture, too. Confluency is the ratio of area occupied by the cells over the total area of the field of view. Topman et al. developed a method in 2011 where the denuded areas in a micrograph of a culture are detected based on standard deviation. This method provides a segmentation of micrograph into two phases: the denuded area and the area populated by cells. To distinguish between these two areas, a threshold filter is applied and the threshold value is identified empirically for each cell type and used for the same cell type for all micrographs. This approach was then used to develop PHANTAST which contained all advancement of phase contrast microscopy image segmentation that was made by Topman et al., 2011.

One of the common quantifications to measure neurite outgrowth aftermath, the treatment regime in the lab is assessing the ability of one neuron to outgrow, and this term is commonly reported as neurite length. Quantification of neurite length can be assessed using NeuronJ plugins in ImageJ. NeuronJ is a semi-automated tracing program used to quantify the length and number of neurites of fluorescent images through immunofluorescence analysis. With NeuronJ, one can trace the fluorescent labeled neurons manually, and the cursor will update and follow the estimated path automatically that is directed by the users to increase the speed and accuracy of the tracing. Besides NeuronJ, AxonTracer is also another plugin, which provides comparable results to NeuronJ. However, AxonTracer is a fully automated plugin that can be used to analyze the axon growth through detection, tracing, and quantification of axon as well as detecting the fluorescent labelled cell graft. It is a useful plugin for the quantification of axon regeneration after spinal cord injury.

The arborization of neurite can be measured using Sholl analysis, which is also one of the functions in ImageJ. Sholl analysis enables the quantification of the number of intersections by dendrites that are present at a fixed distance from the center of the cell body of a neuron, also known as the soma in the concentric circle. The number of branches, branch geometry and the branching patterns of neurons will be discovered through this analysis as well. Sholl analysis helps to analyze the neuronal morphology to determine how information is processed through neurons, how action potential propagates and the neuronal functions. The effects of neurite branching on single neuron integrated synaptic inputs and the communications between the networks can be determined as well. The neuronal morphology can be determined through Sholl analysis of ImageJ or quantification of the number of neurites, tips or branch points.

When quantifying the events after spinal cord injury, we shall include the analysis based on the myelinating cultures of the spinal cord. Myelin is an insulating protein which covers the nerve cells and is highly found in the white matter. Myelin degeneration can be related to the consequences of spinal cord injury, too. Therefore, for the quantification of myelin, MyelinJ from ImageJ has been introduced to produce high throughput analysis of myelin degeneration and regeneration after spinal cord injury. MyelinJ provides results for the analysis of the percentage of myelination and percentage of neurite density of the image analyzed. Compared to another image analysis software, Cellprofiler, MyelinJ gives a higher percentage of 46% for the identification of myelin sheath pixels. MyelinJ also can ignore the non-myelin sheath background, which gives a more accurate result.
Overall, the integration of AI into biomedical applications has opened doors to visualize and analyze the human body in much detail and with more accuracy.

Reference (Mar-21-A8)


About the author: “Creativity is allowing yourself to make mistakes, art is knowing which one to keep”, a quote that I would like to share to everyone. I am a final year student pursuing Biomedicine in Universiti Sains Malaysia. I hope that Biomedical Science and Health Sciences related fields will gain more interest from the Malaysia society. In my final year project, I was focusing on the analysis of the events after spinal cord injury. Therefore, I would like to share some of the knowledge about the use of ImageJ software in analysing the events after spinal cord injury.

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