Assessing microscope image focus quality with deep learning.
Assessing microscope image focus quality with deep learning.
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DOI：
10.1186/s12859-018-2087-4
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发表时间：
2018-03-15
影响因子：
3
通讯作者：
Nelson P
中科院分区：
文献类型：
--
作者：
Yang SJ;Berndl M;Michael Ando D;Barch M;Narayanaswamy A;Christiansen E;Hoyer S;Roat C;Hung J;Rueden CT;Shankar A;Finkbeiner S;Nelson P
在自动显微镜上获取的大型图像数据集通常具有一些低质量的散焦图像，尽管使用硬件自动聚焦系统。使用自动图像分析以高精度识别这些图像对于获得干净、无偏差的图像数据集是重要的。使该任务复杂化的是图像聚焦质量仅在图像的前景区域中被良好定义的事实，并且因此，大多数先前的方法仅使得能够计算两个或更多个图像之间的质量的相对差异，而不是质量的绝对度量。我们提出了一个深度神经网络模型，它能够单独预测单个图像上的图像焦点的绝对度量，而无需任何用户指定的参数。该模型在图像补丁级别运行，并输出预测确定性的度量，从而实现可解释的预测。该模型仅在U2 OS细胞的384个焦点内Hoechst（细胞核）染色图像上训练，这些图像在训练期间被合成地散焦到11个绝对散焦水平之一。训练的模型可以在先前看不见的真实的Hoechst染色图像上概括，以95%的准确度将绝对图像焦点识别到一个散焦水平（大约3像素模糊直径差）内。在一个更简单的二进制在/离焦分类任务中，训练的模型在Hoechst和鬼笔环肽（肌动蛋白）染色图像上的表现优于以前的方法（F分数分别为0.89和0.86，分别超过0.84和0.83），尽管在训练期间只呈现了Hoechst染色图像。最后，我们定性地观察到，该模型推广到两个额外的染色，Hoechst和微管蛋白，一个看不见的细胞类型（人MCF-7）上获得不同的仪器。我们的深度神经网络能够通过可解释的块级焦点和确定性预测，以比以前的方法更高的准确性和精度对失焦显微镜图像进行分类。合成散焦图像的使用排除了对手动注释的训练数据集的需要。该模型还推广到不同的图像和细胞类型。用于模型训练和图像预测的框架可以作为免费软件库使用，预训练模型可以在Fiji（ImageJ）和CellProfiler中立即使用。
Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.
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影响因子：
4.6
作者：
Koho S;Fazeli E;Eriksson JE;Hänninen PE
通讯作者：
Hänninen PE
影响因子：
4.6
作者：
Chen CL;Mahjoubfar A;Tai LC;Blaby IK;Huang A;Niazi KR;Jalali B
通讯作者：
Jalali B
通过深度学习进行盲图像质量评估
DOI：
10.1109/tnnls.2014.2336852
发表时间：
2015-06-01
影响因子：
10.4
作者：
Hou, Weilong;Gao, Xinbo;Li, Xuelong
通讯作者：
Li, Xuelong
影响因子：
48
作者：
Schneider CA;Rasband WS;Eliceiri KW
通讯作者：
Eliceiri KW
影响因子：
--
作者：
Bray MA;Fraser AN;Hasaka TP;Carpenter AE
通讯作者：
Carpenter AE