Artificial neural network-aided image analysis system for cell counting.
Artificial neural network-aided image analysis system for cell counting.
复制标题
用于细胞计数的人工神经网络辅助图像分析系统。
DOI：
10.1002/(sici)1097-0320(19990501)36:1
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
Lars Wahlberg
中科院分区：
文献类型：
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作者：
Per Jesper Sjöström;Beata Ras Frydel;Lars Wahlberg
背景
在含有碎片和合成材料的组织学制备中，难以使用标准图像分析工具自动化细胞计数，即，系统依赖于边界轮廓，直方图阈值处理等。在试图模仿手动细胞识别，自动细胞计数器构建使用人工智能和标准图像分析方法的组合。
方法
人工神经网络（ANN）方法应用于数字化显微镜领域没有预ANN特征提取。在Power Macintosh 7300/180台式计算机上使用误差反向传播算法，在第一隐层中具有广泛权重共享的三层前馈网络被采用并在1，830个示例上进行训练。确定了隐藏神经元的最佳数量，并通过与盲态人类计数进行比较来验证训练后的系统。在50倍和100倍放大倍率下评价系统性能。
结果
在100倍放大率下的相关指数接近人与人之间的差异，而50倍放大率则没有用。这个系统比有经验的人快大约六倍。
结论
基于人工神经网络的自动细胞计数在嘈杂的组织学准备是可行的。一致的组织结构和计算机能力对系统性能至关重要。该系统提供了几个好处，如分析速度和一致性，并释放人员从事其他任务。
BACKGROUND
In histological preparations containing debris and synthetic materials, it is difficult to automate cell counting using standard image analysis tools, i.e., systems that rely on boundary contours, histogram thresholding, etc. In an attempt to mimic manual cell recognition, an automated cell counter was constructed using a combination of artificial intelligence and standard image analysis methods.
METHODS
Artificial neural network (ANN) methods were applied on digitized microscopy fields without pre-ANN feature extraction. A three-layer feed-forward network with extensive weight sharing in the first hidden layer was employed and trained on 1,830 examples using the error back-propagation algorithm on a Power Macintosh 7300/180 desktop computer. The optimal number of hidden neurons was determined and the trained system was validated by comparison with blinded human counts. System performance at 50x and lO0x magnification was evaluated.
RESULTS
The correlation index at 100x magnification neared person-to-person variability, while 50x magnification was not useful. The system was approximately six times faster than an experienced human.
CONCLUSIONS
ANN-based automated cell counting in noisy histological preparations is feasible. Consistent histology and computer power are crucial for system performance. The system provides several benefits, such as speed of analysis and consistency, and frees up personnel for other tasks.