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...

Mechanical feedback from the tumor microenvironment regulates an array of processes underlying cancer biology. For example, increased stiffness of mammary extracellular matrix (ECM) drives malignancy and alters the phenotypes of breast cancer cells. Despite this link, the role of substrate stiffness in chemotherapeutic response in breast cancer remains unclear. This is complicated by routine culture and adaptation of cancer cell lines to unnaturally rigid plastic or glass substrates, leading to profound changes in their growth, metastatic potential and, as we show here, chemotherapeutic response. We demonstrate that primary breast cancer cells undergo dramatic phenotypic changes when removed from the host microenvironment and cultured on rigid surfaces, and that drug responses are profoundly altered by the mechanical feedback cells receive

Analysis of preferential localization of certain genes within the cell nuclei is emerging as a new technique for the diagnosis of breast cancer. Quantitation requires accurate segmentation of 100-200 cell nuclei in each tissue section to draw a statistically significant result. Thus, for large-scale analysis, manual processing is too time consuming and subjective. Fortuitously, acquired images generally contain many more nuclei than are needed for analysis. Therefore, we developed an integrated workflow that selects, following automatic segmentation, a subpopulation of accurately delineated nuclei for positioning of fluorescence in situ hybridization-labeled genes of interest. Segmentation was performed by a multistage watershed-based algorithm and screening by an artificial neural network-based pattern recognition engine. The performance of the workflow was quantified in terms of the fraction of automatically selected nuclei that were visually confirmed as well segmented and by the boundary accuracy of the well-segmented nuclei relative to a 2D dynamic programming-based reference segmentation method. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all four normal cases, and all five noncancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach. Published 2012 Wiley-Periodicals, Inc. y