fastER: a user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy
fastER: a user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy
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DOI：
10.1093/bioinformatics/btx107
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发表时间：
2017-07-01
期刊：
影响因子：
5.8
通讯作者：
Schroeder, Timm
中科院分区：
文献类型：
--
作者：
Hilsenbeck, Oliver;Schwarzfischer, Michael;Schroeder, Timm
动机：定量大规模细胞显微镜广泛应用于生物和医学研究。这样的实验会产生大量的图像数据，因此需要自动分析。然而，细胞轮廓（细胞分割）的自动检测通常是具有挑战性的，由于，例如，高细胞密度，细胞与细胞的变异性和低的信号-噪声ratio.Results：在这里，我们评估的准确性和速度的各种国家的最先进的方法，在光学显微镜图像细胞分割具有挑战性的真实的和合成图像数据。结果在数据集之间存在差异，并且表明测试工具要么不够强大，要么计算昂贵，从而限制了它们在大规模实验中的应用。因此，我们开发了fastER，这是一种可训练的工具，速度快了几个数量级，同时产生了最先进的分割质量。它支持各种细胞类型和图像采集模式，但即使对于非专家也易于使用：它没有参数，可以通过交互式标记细胞进行训练来适应特定的图像集。作为一个概念的证明，我们分割和计数细胞超过200 000明场图像（1388 × 1040像素）从六天的延时显微镜实验;超过46 000 000单细胞的识别只需要约两个半小时的台式电脑。
Motivation: Quantitative large-scale cell microscopy is widely used in biological and medical research. Such experiments produce huge amounts of image data and thus require automated analysis. However, automated detection of cell outlines (cell segmentation) is typically challenging due to, e.g. high cell densities, cell-to-cell variability and low signal-to-noise ratios.Results: Here, we evaluate accuracy and speed of various state-of-the-art approaches for cell segmentation in light microscopy images using challenging real and synthetic image data. The results vary between datasets and show that the tested tools are either not robust enough or computationally expensive, thus limiting their application to large-scale experiments. We therefore developed fastER, a trainable tool that is orders of magnitude faster while producing state-of-the-art segmentation quality. It supports various cell types and image acquisition modalities, but is easy-to-use even for non-experts: it has no parameters and can be adapted to specific image sets by interactively labelling cells for training. As a proof of concept, we segment and count cells in over 200 000 brightfield images (1388 x 1040 pixels each) from a six day time-lapse microscopy experiment; identification of over 46 000 000 single cells requires only about two and a half hours on a desktop computer.