ABV-IIITM, Gwalior, India.
Med Biol Eng Comput. 2020 May;58(5):1127-1146. doi: 10.1007/s11517-020-02135-7. Epub 2020 Mar 19.
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. Graphical Abstract Resultant View of the overall proposed approach.
自动细胞分析方法能够对细胞进行分割，并可检测体内活/死细胞的数量。本研究提出了一种新的非线性分割模型（NSM），用于分割和量化体内的活/死细胞。这项工作还揭示了电磁辐射对细胞体的影响。利用 60×物镜分辨率的大鼠海马 CA3 区明亮图像来分析称为尼氏染色数据集的影响。所提出的非线性分割模型基于线性回归分析，将前景细胞从细胞图像中分割出来。这些前景细胞进一步使用形状描述符和几何方法分别作为活/死细胞进行区分和量化。与现有的知名方法相比，所提出的分割模型显示出有希望的结果（准确性为 82.82%）。使用所提出的方法进行活细胞和死细胞的计数分析明显优于手动计数。因此，所提出的分割模型和量化程序是一种细胞定量的组合方法，可产生更好的分割结果，并提供微观水平上神经元异常分析的简明见解。
