Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology
Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology
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DOI：
10.1016/j.jchemneu.2018.12.010
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发表时间：
2019-03-01
影响因子：
2.8
通讯作者：
Mouton, Peter R.
中科院分区：
文献类型：
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作者：
Alahmari, Saeed S.;Goldgof, Dmitry;Mouton, Peter R.
近几十年来，基于体视学的研究在理解大脑衰老和开发治疗神经系统疾病和精神疾病的新药物发现策略方面发挥了重要作用。神经科学众多分支学科取得进一步进展的一个主要障碍仍然是缺乏用于体视学分析的高通量技术。尽管建立在方法上无偏见的原则上，但市售体视学系统仍然依赖训练有素的人员来手动计数每个感兴趣区域 (ROI) 内的数百个细胞。即使对于 10 只对照动物和 10 只治疗动物的简单研究，细胞计数通常也需要一个多月的繁琐劳动和高昂的成本。此外，由于主观性、训练变量、识别偏差和疲劳等人为因素，这些研究容易出现错误且重复性差。在这里，我们提出了一种深度神经网络-体视学组合来自动分割和估计组织切片上免疫染色神经元的总数。我们的三步方法包括 (1) 从图像的 z 堆栈（解剖器堆栈）创建扩展景深 (EDF) 图像； (2)应用自适应分割算法(ASA)来标记EDF图像中的染色细胞(即，创建掩模)以训练卷积神经网络(CNN)； (3) 使用经过训练的 CNN 模型，使用光学分馏器方法自动分割和计算测试解剖器堆栈中的细胞总数。与受过训练的人类计数相比，自动化体视学方法显示出小于 2% 的误差和超过 5 倍的效率，并且没有传统体视学相关的主观性、单调性和较差的精度。
In recent decades stereology-based studies have played a significant role in understanding brain aging and developing novel drug discovery strategies for treatment of neurological disease and mental illness. A major obstacle to further progress in a wide range of neuroscience sub-disciplines remains the lack of high-throughput technology for stereology analyses. Though founded on methodologically unbiased principles, commercially available stereology systems still rely on well-trained humans to manually count hundreds of cells within each region of interest (ROI). Even for a simple study with 10 controls and 10 treated animals, cell counts typically require over a month of tedious labor and high costs. Furthermore, these studies are prone to errors and poor reproducibility due to human factors such as subjectivity, variable training, recognition bias, and fatigue. Here we propose a deep neural network-stereology combination to automatically segment and estimate the total number of immunostained neurons on tissue sections. Our three-step approach consists of (1) creating extended depth-of-field (EDF) images from z-stacks of images (disector stacks); (2) applying an adaptive segmentation algorithm (ASA) to label stained cells in the EDF images (i.e., create masks) for training a convolutional neural network (CNN); and (3) use the trained CNN model to automatically segment and count the total number of cells in test disector stacks using the optical fractionator method. The automated stereology approach shows less than 2% error and over 5x greater efficiency compared to counts by a trained human, without the subjectivity, tedium, and poor precision associated with conventional stereology.