A high‐throughput system for segmenting nuclei using multiscale techniques

Abstract

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 AE 0.3% and 95.5 AE 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 lm ($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. Key termshigh-throughput segmentation; multiscale edge representation; nonuniform illumination; multiscale thresholding; watershed; region merging; cluster analysis IN recent years, investigations on nuclear architecture (spatial organization of nuclear organelles into well-defined compartments) and nonrandom gene positioning show significant impact on protein expression and cell functions (1-4). In these studies, the investigators used 2-D spatial distributions of relative and radial distances of fluorescence in situ hybridization (FISH)-labeled DNA sequences in interphase nuclei. They established the correlation between the spatial proximity of translocation prone genes and carcinogenesis (5-7). The motivation for this article stems from this interesting application in genomic organization. Our ultimate goal is to develop an exploratory data analysis system for studying the correlation between genomic organization and carcinogenesis (8). Naturally, such a system requires a segmentation method that can efficiently extract nuclei from fluorescence images and also possesses a high degree of segmentation accuracy (inaccurate segmentation ca...

Automatic segmentation and supervised learning‐based selection of nuclei in cancer tissue images

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Single‐cell‐based image analysis of high‐throughput cell array screens for quantification of viral infection

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