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首页> 外文期刊>Journal of chemical neuroanatomy >Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology
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Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology

机译：通过深度学习和无偏见的立体学自动化细胞计数组织部分

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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.
机译：近几十年来，基于立体学的研究在了解脑老化和发展新药发现策略方面发挥了重要作用，以治疗神经疾病和精神疾病。在广泛的神经科学子学中进一步进展的主要障碍仍然是立体分析的缺乏高通量技术。虽然在方法上取得了无偏见的原则，但商业上可用的立体系统仍然依赖于训练有素的人类，以手动计算每个感兴趣区域内的数百个细胞（ROI）。即使对于具有10个对照和10种治疗动物的简单研究，细胞计数通常需要超过一个月的繁琐劳动和高成本。此外，由于人为因素，诸如主观性，可变培训，识别偏差和疲劳等人类因素，这些研究易于误差和不良再现性。在这里，我们提出了一种深度神经网络 - 立体学组合，以自动分割和估计组织切片上的免疫染色神经元的总数。我们的三步方法包括（1）从Z堆叠（挡块堆叠）中创建扩展景深（EDF）图像; （2）将自适应分割算法（ASA）应用于EDF图像中的标记染色细胞（即，创建掩码）以训练卷积神经网络（CNN）; （3）使用训练的CNN模型自动段，并使用光学分数法将测试障碍堆栈中的单元总数计数。自动化立体方法的误差显示误差低于2％，而培训的人类的计数相比，效率超过5倍，而没有与传统立体中的差异相关的主观性，乏味和差的精度。

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