Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
Bioinformatics. 2017 Jul 1;33(13):2020-2028. doi: 10.1093/bioinformatics/btx107.
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.
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 × 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.
C ++ code, binaries and data at https://www.bsse.ethz.ch/csd/software/faster.html .
oliver.hilsenbeck@bsse.ethz.ch or timm.schroeder@bsse.ethz.ch.
Supplementary data are available at Bioinformatics online.
定量大规模细胞显微镜广泛用于生物和医学研究。此类实验产生大量的图像数据，因此需要自动分析。然而，由于细胞密度高、细胞间变异性大、信噪比低等原因，自动检测细胞轮廓（细胞分割）通常具有挑战性。
在这里，我们使用具有挑战性的真实和合成图像数据评估了各种用于明场显微镜图像细胞分割的最先进方法的准确性和速度。结果因数据集而异，表明所测试的工具要么不够稳健，要么计算成本过高，因此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，速度快几个数量级，同时产生最先进的分割质量。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。作为概念验证，我们在一个六天的延时显微镜实验中分割和计数了超过 200000 张明场图像（每个图像为 1388×1040 像素）；识别超过 46000000 个单细胞仅在台式计算机上需要大约两个半小时。
C++代码、二进制文件和数据可在 https://www.bsse.ethz.ch/csd/software/faster.html 获得。
oliver.hilsenbeck@bsse.ethz.ch 或 timm.schroeder@bsse.ethz.ch。
补充数据可在生物信息学在线获得。
