A multi‐model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images

Abstract

Automated segmentation and morphometry of fluorescently labeled cell nuclei in batches of 3D confocal stacks is essential for quantitative studies. Model-based segmentation algorithms are attractive due to their robustness. Previous methods incorporated a single nuclear model. This is a limitation for tissues containing multiple cell types with different nuclear features. Improved segmentation for such tissues requires algorithms that permit multiple models to be used simultaneously. This requires a tight integration of classification and segmentation algorithms. Two or more nuclear models are constructed semiautomatically from user-provided training examples. Starting with an initial over-segmentation produced by a gradient-weighted watershed algorithm, a hierarchical fragment merging tree rooted at each object is built. Linear discriminant analysis is used to classify each candidate using multiple object models. On the basis of the selected class, a Bayesian score is computed. Fragment merging decisions are made by comparing the score with that of other candidates, and the scores of constituent fragments of each candidate. The overall segmentation accuracy was 93.7% and classification accuracy was 93.5%, respectively, on a diverse collection of images drawn from five different regions of the rat brain. The multi-model method was found to achieve high accuracy on nuclear segmentation and classification by correctly resolving ambiguities in clustered regions containing heterogeneous cell populations. ' International Society for Analytical CytologyKey terms cell nuclei; segmentation; classification; watershed algorithm; region merging; modelbased; Bayesian estimator; parzen window; batch processing; 3D confocal microscopy THREE-dimensional (3D) segmentation of fluorescently labeled cell nuclei in confocal image stacks is an essential image analysis task required in numerous quantitative studies (1-3). The results of nuclear segmentation can be used for counting, morphometry, classification, and associative measurement of secondary fluorescent markers (3,4). As an example, Figure 1 shows fluorescently labeled neuronal and glial cell nuclei from various rat brain regions, drawn from a study involving compartmental and temporal analysis of FISH (fluorescence in situ hybridization) signals (3D-cat-FISH). Nuclear segmentation is an essential first step to quantitating FISH data (4,5).The seemingly straightforward task of segmenting blob-like nuclei continues to present challenges, especially in high-throughput applications requiring high levels of accuracy, automation, reliability, and speed. Most of the challenges are rooted in the complexity and variability of nuclear appearance across images, staining and imaging protocols, and ambiguities associated with tight clustering of objects (6,7). Other sources of error include nonuniform staining, imaging artifacts such as depth-

Associative image analysis: A method for automated quantification of 3D multi-parameter images of brain tissue

“…Even with the linear unmixing based complexity reduction, sophisticated automated segmentation algorithms were required for this effort. As examples, the multiple-model nuclear segmentation algorithm (Lin et al, 2007), and the vessel segmentation algorithm (Supplement C) are recent developments. In addition, this work has leveraged recent advances in image processing.…”

“…2. Fist, the CyQuant-labeled cell nuclei were segmented using a previously reported algorithm (Lin et al, 2007), and the results are shown in Fig 3a. Briefly, the images were de-noised using a median filter (width=5), followed by morphological opening and closing operations. The kernels for both morphological operations were ellipsoids with a major axis of width w 1 =5, and minor axis of width w 2 =max{w 1 × (d xy /d z ), 3}, where d xy and d z were the voxel sizes along the lateral and axial directions, respectively.…”

“…Automated algorithms have been developed to delineate individual elements of neuroanatomy, e.g., counting of nuclei, tracing neuronal processes, branching pattern of dendrites for a single neuron, and vascular tracing Lin et al, 2005a;Tyrrell JA et al, 2007;Fernandez-Gonzalez et al, 2002;Weaver et al, 2004;Lin et al, 2007;Zhang et al, 2007;Al-Kofahi et al, 2007). These tools are able to delineate a specific class of structures from single channel images with error rates in the range of 5 -10%, and have progressed greatly in their ability to handle morphological variability.…”

Three‐dimensional segmentation of nuclei and mitotic chromosomes for the study of cell divisions in live Drosophila embryos

“…Because objects in deeper regions might be incompletely recorded and, in single-photon LSCM, deeper sections are affected by light attenuation and scattering, we only scored objects whose centroids had z-coordinates of 25 lm or less. We compared our segmentation method to a variant of Otsu's adaptive thresholding (13) and the 64-bit version of the FARSIGHT nuclear segmentation (40), which is based on a combination of graph cuts and multiscale LOG. We did not use methods that require commercial software licenses, for example, Matlab.…”

“…To further evaluate our segmentation method, we compared it with a variant of Otsu's adaptive thresholding (13) and the automated nuclear segmentation module of the FAR-SIGHT toolkit (20,40). Our intention was to assess how existing open-source tools developed for different 3D images of fluorescently labeled nuclei performs on our data.…”