Automatic classification of sub-microlitre protein-crystallization trials in 1536-well plates
Automatic classification of sub-microlitre protein-crystallization trials in 1536-well plates
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DOI：
10.1107/s0907444903015130
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发表时间：
2003-09-01
期刊：
影响因子：
--
通讯作者：
Jurisica, I
中科院分区：
文献类型：
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作者：
Cumbaa, CA;Lauricella, A;Jurisica, I
描述了一种自动评价微批（400nl）蛋白质结晶试验的技术。该方法解决了在亚微升尺度上引入的分析问题，包括不均匀的光照和不规则的液滴边界。液滴使用具有两层网格拓扑结构的环形概率图形模型从井中分割。利用Radon变换提取直边特征，利用一组相关滤波器提取微晶特征，从液滴图像中提取23个特征向量。图像分类是通过特征向量的线性判别分析实现的。将自动方法的结果与人类专家在32个1536孔板上的结果进行了比较。使用人为标记的图像作为ground truth，该方法对图像进行分类，准确率为85%，ROC分数为0.84。该结果与实验重复性率（87%）比较良好。被错误归类为晶体阳性的图像包含各种类似微晶体的斑点沉淀，皮肤效应或被人类专家错误标记的真正晶体。许多被错误地归类为晶体阴性的图像包含各种非常精细的晶体特征或缺乏直边的树突。这些错误分类的特征提出了改进方法的方向。
A technique for automatically evaluating microbatch (400 nl) protein-crystallization trials is described. This method addresses analysis problems introduced at the sub-microlitre scale, including non-uniform lighting and irregular droplet boundaries. The droplet is segmented from the well using a loopy probabilistic graphical model with a two-layered grid topology. A vector of 23 features is extracted from the droplet image using the Radon transform for straight-edge features and a bank of correlation filters for microcrystalline features. Image classification is achieved by linear discriminant analysis of its feature vector. The results of the automatic method are compared with those of a human expert on 32 1536-well plates. Using the human-labeled images as ground truth, this method classifies images with 85% accuracy and a ROC score of 0.84. This result compares well with the experimental repeatability rate, assessed at 87%. Images falsely classified as crystal-positive variously contain speckled precipitate resembling microcrystals, skin effects or genuine crystals falsely labeled by the human expert. Many images falsely classified as crystal-negative variously contain very fine crystal features or dendrites lacking straight edges. Characterization of these misclassifications suggests directions for improving the method.