Abstract
Microglial cells are now recognized as crucial players in the development of neurodegenerative diseases. The analysis and quantification of microglia changes is necesary to better understand the contribution of microglia in neurodegenerative processes or drug treatments. However, the manual quantification of microglial cells is a time-consuming and subjective tasks; and, therefore, reliable tools that automate this process are desirable. In this paper, we present MicrogliaJ, an ImageJ macro, that can measure both the number and area of microglial cells. The automatic procedure implemented in MicrogliaJ is based on classical image processing techniques, and the results can be manually validated by experts with a simple-to-use interface. MicrogliaJ has been tested by experts and it obtains analogous results to those manually produced, but considerably reducing the time required for such analysis. Thanks to this work, the analysis of microglia images will be faster and more reliable, and this will help us to advance our understanding of the behaviour of microglial cells.
This work was partially supported by Grant PID2020-115225RB-I00 funded by MCIN/AEI/ 10.13039/501100011033.
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Casado-García, Á. et al. (2023). MicrogliaJ: An Automatic Tool for Microglial Cell Detection and Segmentation. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_47
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DOI: https://doi.org/10.1007/978-3-031-36616-1_47
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