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Discrimination and quantification of live/dead rat brain cells using a non-linear segmentation model

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Abstract

The automatic cell analysis method is capable of segmenting the cells and can detect the number of live/dead cells present in the body. This study proposed a novel non-linear segmentation model (NSM) for the segmentation and quantification of live/dead cells present in the body. This work also reveals the aspects of electromagnetic radiation on the cell body. The bright images of the hippocampal CA3 region of the rat brain under the resolution of 60 × objective are used to analyze the effects called NISSL-stained dataset. The proposed non-linear segmentation model segments the foreground cells from the cell images based on the linear regression analysis. These foreground cells further get discriminated as live/dead cells and quantified using shape descriptors and geometric method, respectively. The proposed segmentation model is showing promising results (accuracy, 82.82%) in comparison with the existing renowned approaches. The counting analysis of live and dead cells using the proposed method is far better than the manual counts. Therefore, the proposed segmentation model and quantifying procedure is an amalgamated method for cell quantification that yields better segmentation results and provides pithy insights into the analysis of neuronal anomalies at a microscopic level.

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Acknowledgments

The authors would like to acknowledge that the work is supported by Department of Technology (DoT) and Department of Science and Technology (DST), India. Dataset used in the proposed work is supported and provided by the Manipal University, Department of Life Science Engineering, Karnataka, India.

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  1. ABV-IIITM, Gwalior, India

    Mukta Sharma & Mahua Bhattacharya

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  1. Mukta Sharma
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  2. Mahua Bhattacharya
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Correspondence to Mukta Sharma.

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Sharma, M., Bhattacharya, M. Discrimination and quantification of live/dead rat brain cells using a non-linear segmentation model. Med Biol Eng Comput 58, 1127–1146 (2020). https://doi.org/10.1007/s11517-020-02135-7

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  • DOI: https://doi.org/10.1007/s11517-020-02135-7

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