Other programs like NeuroMantic, NeuronStudio, and Vaa3D use semi-automatic reconstruction for faster analysis, but the emergence of fully automated algorithms has notably accelerated the image analysis process 6, 7, 8, 9. However, this process is time-consuming and biased due to human subjectivity and is, therefore, typically used for tracing only a few sections of neurons. These scripts, however, often struggle with accurate recognition of neural bodies and projections, particularly in low-contrast or noisy images, do not support 3D analyses, and demand manual steps that prolong analysis time.Īnalysing neural cell projections commonly involves laborious manual or semi-manual methods in programs like NeuronJ 5. Therefore, plugins and macros are created to surpass manual analysis, accelerating and optimizing image analysis in open-source software. On the other hand, open-source platforms like ImageJ and Cell Profiler lack specialization for detailed analyses of elongated shapes of biological structures 3, 4. Although powerful tools, Imaris might still require some manual interventions during the analysis, while MATLAB’s 3DMorph script, developed primarily for the analysis of 3D microglial morphology, can yield false positive or false negative results when analysing neurons, since it includes neuronal bodies in length measurements 2. Nowadays, specialized platforms as Imaris or MATLAB offer analysis options, but they require a substantial budget. Algorithms that automatically recognize, separate, and quantify these elements are crucial for detailed analysis of such images as manual measurements, though meticulous, are time-consuming, thereby often limiting analyses to just a fraction of cell populations 1. Moreover, even the subcellular structures, like mitochondria, or supracellular, like vessels, adapt their shapes based on the cellular or tissue environment.Īdvanced cell and tissue visualization methods generate a vast number of images filled with numerous complex cellular and subcellular structures. Rather, they dynamically adjust their morphology in response to the physiological and pathological changes within tissue. In addition, such projections, and their endings, crucial for cellular communication, are not static. Neurons, for instance, possess projections, axons and dendrites, that vary in size and intermingle with other cellular structures to form very complex morphologies. The human body is composed of diverse cell types and structures with various shapes. Lusca’s automated and precise measurement capability makes it an ideal choice for diverse biological image analyses. Its implementation of machine learning-based segmentation facilitates versatile applications for different cell types and biological structures, including mitochondria, fibres, and vessels. With three times larger execution speed, fewer false positive and negative results, and the capacity to measure various parameters, Lusca surpasses other existing open-source solutions. Consequently, it ensures quicker and more precise quantification by effectively eliminating noise and discerning subtle details. This is a significant enhancement compared to other scripts that offer only 5–15 parameters. Moreover, in conjunction with colocalization measurements, it provides an extensive set of 29 morphometric parameters for both 2D and 3D analysis. It performs particle analysis to ascertain parameters such as area/volume, quantity, and intensity, in addition to skeletonization for determining length, branching, and width. Lusca utilizes machine learning for image segmentation with intensity and size thresholds. Aiming to enhance image analysis of complex biological forms including neurons using available FIJI (ImageJ) plugins, Lusca, an advanced open-source tool, was developed. Neurons possess varying-sized projections that interact with different cellular structures leading to the development of highly complex morphologies. The human body consists of diverse subcellular, cellular and supracellular structures.
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