A high-throughput system for segmenting nuclei using multiscale techniques
A high-throughput system for segmenting nuclei using multiscale techniques
复制标题
DOI：
10.1002/cyto.a.20550
复制
发表时间：
2008-05-01
期刊：
影响因子：
3.7
通讯作者：
Lockett, S. J.
中科院分区：
文献类型：
--
作者：
Gudla, Prabhakar R.;Nandy, K.;Lockett, S. J.
细胞核的自动分割在多种高通量细胞计数应用中至关重要，而手动分割则费力且不可重复。此类新兴应用之一是测量荧光原位杂交 (FISH) DNA 序列的空间组织（径向和相对距离），最近的研究强烈表明基因的非随机排列与致癌之间存在相关性。当前的自动分割方法在存在不均匀照明和聚类的情况下具有不同的性能，并且很少评估边界精度，这使得它们对于该应用来说不是最佳的。作者提出了一种用于提取单个核的模块化和基于模型的算法。它使用多尺度边缘重建来进行对比度拉伸和边缘增强，以及使用基于多尺度熵的阈值来处理不均匀的强度变化。核最初被过度分割，然后根据面积合并，然后自动多级分类为单核和簇核。输入参数的估计和分类器的训练是自动的。该算法在 4,181 个具有不同程度背景不均匀性和聚类的淋巴母细胞核上进行了测试。它提取了 3,515 个单个细胞核，并以 99.8 +/- 0.3% 和 95.5 +/- 5.1% 的准确度分别识别单个细胞核和簇中的单个细胞核。与手动分割相比，单个核的分割边界是准确的，平均 RMS 偏差为 0.26 μm（类似于 2 个像素）。所提出的分割方法对于从包含簇和孤立核的荧光图像中分割单个核来说是高效、稳健和准确的。该算法允许完全自动化，并促进 DNA 序列的可重复且无偏见的空间分析。 2008 年出版 Wiley-Liss, Inc.
Automatic segmentation of cell nuclei is critical in several high-throughput cytometry applications whereas manual segmentation is laborious and irreproducible. One such emerging application is measuring the spatial organization (radial and relative distances) of fluorescence in situ hybridization (FISH) DNA sequences, where recent investigations strongly suggest a correlation between nonrandom arrangement of genes to carcinogenesis. Current automatic segmentation methods have varying performance in the presence of nonuniform illumination and clustering, and boundary accuracy is seldom assessed, which makes them suboptimal for this application. The authors propose a modular and model-based algorithm for extracting individual nuclei. It uses multiscale edge reconstruction for contrast stretching and edge enhancement as well as a multiscale entropy-based thresholding for handling nonuniform intensity variations. Nuclei are initially oversegmented and then merged based on area followed by automatic multistage classification into single nuclei and clustered nuclei. Estimation of input parameters and training of the classifiers is automatic. The algorithm was tested on 4,181 lymphoblast nuclei with varying degree of background nonuniformity and clustering. It extracted 3,515 individual nuclei and identified single nuclei and individual nuclei in clusters with 99.8 +/- 0.3% and 95.5 +/- 5.1% accuracy, respectively. Segmented boundaries of the individual nuclei were accurate when compared with manual segmentation with an average RMS deviation of 0.26 mu m (similar to 2 pixels). The proposed segmentation method is efficient, robust, and accurate for segmenting individual nuclei from fluorescence images containing clustered and isolated nuclei. The algorithm allows complete automation and facilitates reproducible and unbiased spatial analysis of DNA sequences. Published 2008 Wiley-Liss, Inc.