• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Acta crystallographica.Section D. Biological crystallography >Automatic classification of sub-microlitre protein-crystallization trials in 1536-well plates.
【24h】

Automatic classification of sub-microlitre protein-crystallization trials in 1536-well plates.

机译：自动分类sub-microlitre蛋白质晶体试验在1536年盘子。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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.
机译：一种技术来自动评估microbatch (400 nl)蛋白质晶体试验。介绍了在分析问题sub-microlitre规模,包括不均匀照明和不规则的液滴的界限。使用一个呆头呆脑的液滴从井中分割概率图形模型与两层网格拓扑。使用氡从液滴图像中提取变换校正装置特性和一家银行微晶的相关性过滤器特性。线性判别分析的功能向量。相比人类专家321536 -孔板。地面实况,这方法分类图像85%的准确率和ROC得分为0.84。与实验结果比较好可重复性,评估为87%。错误归类为crystal-positive不同含有斑点沉淀类似微晶核,皮肤效果或真正的晶体虚假标签由人类专家。错误归类为crystal-negative不同包含非常好的晶体特性或树突缺乏直边。误分类显示方向改善的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号