Lusca: FIJI (ImageJ) based tool for automated morphological analysis of cellular and subcellular structures

Iva Šimunić ^1,2, Denis Jagečić ^3,4, Jasmina Isaković ⁵

⁰Department of Histology and Embryology, University of Zagreb School of Medicine, 10000, Zagreb, Croatia. iva.simunic@mef.hr.

Scientific Reports +

|

March 29, 2024

View abstract on PubMed

Summary

Lusca, a new open-source tool, enhances biological image analysis using machine learning for precise quantification of complex cell structures. It offers faster, more accurate measurements than existing solutions.

Area Of Science

Cell biology
Bioimage analysis
Computational biology

Background

The human body features diverse cellular structures, with neurons exhibiting complex morphologies due to varying projection interactions.
Accurate image analysis is crucial for understanding these complex biological forms, but existing tools often lack comprehensive parameter quantification.

Purpose Of The Study

To develop Lusca, an advanced open-source tool for enhanced image analysis of complex biological structures, particularly neurons.
To improve the speed, precision, and parameter range of biological image quantification compared to existing FIJI (ImageJ) plugins.

Main Methods

Lusca employs machine learning for image segmentation using intensity and size thresholds.
It performs particle analysis (area/volume, quantity, intensity) and skeletonization (length, branching, width).
The tool integrates colocalization measurements, providing 29 morphometric parameters for 2D and 3D analysis.

Main Results

Lusca offers a significant enhancement with 29 morphometric parameters, surpassing other scripts that provide 5-15 parameters.
It achieves three times faster execution speed with fewer false positive and negative results.
The tool effectively eliminates noise and discerns subtle details for quicker and more precise quantification.

Conclusions

Lusca provides automated and precise measurement capabilities, making it ideal for diverse biological image analyses.
Its machine learning-based segmentation allows versatile applications for various cell types and biological structures like mitochondria, fibres, and vessels.
Lusca represents a significant advancement over existing open-source solutions for bioimage analysis.

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