In biomedical research, cell analysis is important to assess physiological and pathophysiological information. Virtual microscopy offers the unique possibility to study the compositions of tissues at a cellular scale. However, images acquired at such high spatial resolution are massive, contain complex information, and are therefore difficult to analyze automatically. In this article, we address the problem of individualization of size-varying and touching neurons in optical microscopy two-dimensional (2-D) images. Our approach is based on a series of processing steps that incorporate increasingly more information. (1) After a step of segmentation of neuron class using a Random Forest classifier, a novel min-max filter is used to enhance neurons' centroids and boundaries, enabling the use of region growing process based on a contour-based model to drive it to neuron boundary and achieve individualization of touching neurons. (2) Taking into account size-varying neurons, an adaptive multiscale procedure aiming at individualizing touching neurons is proposed. This protocol was evaluated in 17 major anatomical regions from three NeuN-stained macaque brain sections presenting diverse and comprehensive neuron densities. Qualitative and quantitative analyses demonstrate that the proposed method provides satisfactory results in most regions (e.g., caudate, cortex, subiculum, and putamen) and outperforms a baseline Watershed algorithm. Neuron counts obtained with our method show high correlation with an adapted stereology technique performed by two experts (respectively, 0.983 and 0.975 for the two experts). Neuron diameters obtained with our method ranged between 2 and 28.6 μm, matching values reported in the literature. Further works will aim to evaluate the impact of staining and interindividual variability on our protocol.

中文翻译：

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猕猴大脑显微图像中大小变化和接触神经元的自动个体化。

在生物医学研究中，细胞分析对于评估生理和病理生理信息非常重要。虚拟显微镜提供了在细胞尺度上研究组织成分的独特可能性。然而，以如此高的空间分辨率获取的图像数量巨大，包含复杂的信息，因此难以自动分析。在本文中，我们解决了光学显微镜二维 (2-D) 图像中大小变化和接触神经元的个体化问题。我们的方法基于一系列包含越来越多信息的处理步骤。(1) 在使用随机森林分类器对神经元类别进行分割之后，使用新颖的最小-最大滤波器来增强神经元的质心和边界，从而能够使用基于轮廓模型的区域生长过程来驱动它到神经元边界并实现接触神经元的个体化。(2)考虑到神经元大小的变化，提出了一种旨在个性化接触神经元的自适应多尺度程序。该协议在三个 NeuN 染色的猕猴大脑切片的 17 个主要解剖区域中进行了评估，呈现出多样化和全面的神经元密度。定性和定量分析表明，所提出的方法在大多数区域（例如尾状核、皮质、下托和壳核）提供了令人满意的结果，并且优于基线分水岭算法。用我们的方法获得的神经元计数显示与两位专家执行的适应性体视学技术高度相关（两位专家分别为 0.983 和 0.975）。用我们的方法获得的神经元直径范围在 2 至 28.6 μm 之间，与文献中报道的值相匹配。进一步的工作将旨在评估染色和个体间变异对我们协议的影响。