A high-throughput system for segmenting nuclei using multiscale techniques - PubMed (original) (raw)

A high-throughput system for segmenting nuclei using multiscale techniques

Prabhakar R Gudla et al. Cytometry A. 2008 May.

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 +/- 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 microm (approximately 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.

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Figures

Figure 1.

Figure 1.

Flow diagram illustrating the modular framework of the proposed high-throughput segmentation system.

Figure 2.

Figure 2.

Laplacian tree structure used in the multiscale entropybased thresholding technique.

Figure 3.

Figure 3.

Results from various steps of the proposed algorithm. (A) 2-D image with DAPI stained nuclei, scale bar at top right corner = 15 _μ_m; (B) reconstructed image after applying MEE; (C) output from the multiscale entropy-based thresholding of reconstructed image; (D) labeled objects from Figure 3B used for calculating the minimum nuclear area (objects along the image edges are ignored during the estimation process); (E) result of clustering the size of the labeled objects in Figure 3D into four classes using _K_-means; (F) gradient magnitude of the reconstructed image in Figure 2B; (G) result of watershed segmentation on the gradient magnitude image; (H) result of region merging based on maximum depth and minimum nuclear area constraints and after removing the edge nuclei; (I) result from multistage classifier (edge nuclei have been discarded), red color: single nuclei (Sa,SN); and blue color: clustered nuclei (Sa,CN). In Figures 3F–3H, solid-line arrow and dashed-line arrow point to an isolated nucleus and overlapping nuclei without any edge information between them and the effect of watershed and region merging, respectively. In Figures 3G–3H, dashed rectangle is used for illustrating the declustering of closely packed nuclei and corresponds to a clustered region in MET thresholded image (shown in Figs. 3D–3E).

Figure 4.

Figure 4.

Comparison of edge enhancement from MEE and corner-preserving filtering (CPF). (A) Original image along with nuclei selected for analysis (red color polygons); (B) MEE image along with nuclei selected for analysis (green color polygons); (C) edge enhanced image after CPF along with nuclei selected for analysis (blue color polygons); (D) error-bar plot showing EI [Eq. (13)] for the nuclei selected from the original, MEE, and CPF images. (E) intensity line profiles from original, MEE, and CPF images across two closely packed nuclei selected from top-middle region (shown as a dashed rectangle with a vertical line in Figs. 4A–4C). The dashed horizontal line represents the approximate location where the two nuclei are separated.

Figure 5.

Figure 5.

Quantitative comparison of the boundaries from automatic segmentation and manual segmentation. (A) The red and green color borders correspond to automatic and manual boundaries, respectively. The numbers inside each object is the RMS deviation (in pixels). (B)–(D) zoomed-in version of regions (B), (C), and (D) in Figure 5A. The arrows with triangular heads point to the “jump across” effect and the arrows with square heads point to nuclei with large RMSD because of improper breakage of a cluster. (E) RMSD (in pixels) for each dataset, triangular marker points are the mean values the blue bars denote ± 1 standard deviation. The solid red line is the average RMSD (1.75 pixels) for nuclei from datasets and the dashed red lines (above and below) the solid line denote the ± 1 standard deviation (0.94 pixels).

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