Lockett S J, Herman B
Lawrence-Berkeley Laboratory, California 94720.
Cytometry. 1994 Sep 1;17(1):1-12. doi: 10.1002/cyto.990170102.
Automatic image-based cytometry (IC) can conveniently quantify the distributions of several specific, fluorescence-labeled molecules within individual, isolated cells of slide- or tissue-based specimens. However, many specimens contain clusters of cells or nuclei that are not detected as individual entities by existing automatic methods. We have developed analysis algorithms which detected individual nuclei occurring in clusters or as isolated nuclei. Specimens were labeled with a fluorescent DNA stain, imaged and the images were segmented into regions of nuclei and background. Clusters of nuclei, identified by their size and shape, were divided into individual nuclei by searching for dividing paths between nuclei. The paths, which need not be straight, possessed the highest average gradient per pixel. In addition, both high- and low-pass filtered images of the original image were analyzed. For each individual nucleus, one of the three segmented regions representing the nucleus (from either the original or one of two filtered images) was chosen as the final result, based on the closeness of the regions to average nuclear morphology. The algorithms correctly detected a high proportion of isolated (328/333) and clustered (254/271) nuclei when applied to images of 2 microns prostate and breast cancer sections. Thus, these algorithms should enable much more accurate detection and analyses of nuclei in intact specimens.
基于图像的自动细胞计数法（IC）能够便捷地对玻片或组织标本中单个分离细胞内几种特定的荧光标记分子的分布进行定量分析。然而，许多标本包含细胞或细胞核簇，现有自动方法无法将其检测为单个实体。我们开发了分析算法，可检测出成簇出现的单个细胞核或孤立的细胞核。标本用荧光DNA染料标记、成像，然后将图像分割为细胞核区域和背景区域。通过搜索细胞核之间的分隔路径，根据细胞核的大小和形状识别出的细胞核簇被分割为单个细胞核。这些路径不一定是直的，每个像素具有最高的平均梯度。此外，还对原始图像的高通和低通滤波图像进行了分析。对于每个单个细胞核，根据这些区域与平均核形态的接近程度，从代表细胞核的三个分割区域（来自原始图像或两个滤波图像之一）中选择一个作为最终结果。当将这些算法应用于2微米厚的前列腺癌和乳腺癌切片图像时，能够正确检测出高比例的孤立细胞核（328/333）和成簇细胞核（254/271）。因此，这些算法应能更准确地检测和分析完整标本中的细胞核。
