Chalfoun Joe, Majurski Michael, Dima Alden, Stuelten Christina, Peskin Adele, Brady Mary
Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
Laboratory of Cellular and Molecular Biology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
BMC Bioinformatics. 2014 Dec 30;15(1):431. doi: 10.1186/s12859-014-0431-x.
Many cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single cells continues to be a major problem for methods based on morphological watershed due to the high level of noise in microscopy cell images. There is a need for a new segmentation method that is robust over a wide variety of biological images and can accurately separate individual cells even in challenging datasets such as confluent sheets or colonies.
We present a new automated segmentation method called FogBank that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation. First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce. We evaluated the segmentation accuracy against manually segmented datasets using two metrics. FogBank achieved segmentation accuracy on the order of 0.75 (1 being a perfect match). We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets. FogBank outperformed all related algorithms. The accuracy has also been visually verified on data sets with 14 cell lines across 3 imaging modalities leading to 876 segmentation evaluation images.
FogBank produces single cell segmentation from confluent cell sheets with high accuracy. It can be applied to microscopy images of multiple cell lines and a variety of imaging modalities. The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.
目前医学研究中使用的许多细胞系，如癌细胞或干细胞，以汇合的片层或集落形式生长。单个细胞的生物学特性提供了有价值的信息，因此在这些显微镜图像中分离相互接触的细胞对于单个细胞的计数、识别和测量至关重要。由于显微镜细胞图像中的噪声水平较高，基于形态学分水岭的方法中单个细胞的过度分割仍然是一个主要问题。需要一种新的分割方法，该方法在各种生物图像上都具有鲁棒性，并且即使在诸如汇合片层或集落等具有挑战性的数据集中也能准确分离单个细胞。
我们提出了一种名为FogBank的新的自动分割方法，该方法在细胞汇合且相互接触时能准确分离细胞。该技术已成功应用于相差显微镜、明场显微镜、荧光显微镜和二值图像。该方法基于形态学分水岭原理，并具有两个新特性以提高准确性并最小化过度分割。首先，FogBank使用直方图装箱来量化像素强度，从而将导致过度分割的图像噪声降至最低。其次，与其他类似分水岭算法产生的更线性的细胞边缘不同，FogBank使用从原始图像导出的测地距离掩码来检测单个细胞的形状。我们使用两个指标针对手动分割的数据集评估了分割准确性。FogBank实现的分割准确性约为0.75（1表示完美匹配）。我们在参考数据集上的性能方面将我们的方法与其他可用的分割技术进行了比较。FogBank优于所有相关算法。通过对跨越3种成像模式的14个细胞系的数据集进行视觉验证，也验证了其准确性，共生成了876张分割评估图像。
FogBank能从汇合的细胞片中高精度地分割单个细胞。它可应用于多种细胞系的显微镜图像和各种成像模式。该分割方法的代码以开源形式提供，并包括一个图形用户界面以便于用户友好地执行。
