Background: Automated segmentation of fluorescentlylabeled cell nuclei in 3D confocal microscope images is essential to many studies involving morphological and functional analysis. A common source of segmentation error is tight clustering of nuclei. There is a compelling need to minimize these errors for constructing highly automated scoring systems. Methods: A combination of two approaches is presented. First, an improved distance transform combining intensity gradients and geometric distance is used for the watershed step. Second, an explicit mathematical model for the anatomic characteristics of cell nuclei such as size and shape measures is incorporated. This model is constructed automatically from the data. Deliberate initial over-segmentation of the image data is performed, followed by statistical model-based merging. A confidence score is computed for each detected nucleus, measuring

Background: Automated segmentation of fluorescently labeled cell nuclei in three-dimensional confocal images is essential for numerous studies, e.g., spatiotemporal fluorescence in situ hybridization quantification of immediate early gene transcription. High accuracy and automation levels are required in high-throughput and large-scale studies. Common sources of segmentation error include tight clustering and fragmentation of nuclei. Previous regionbased methods are limited because they perform merging of two nuclear fragments at a time. To achieve higher accuracy without sacrificing scale, more sophisticated yet computationally efficient algorithms are needed. Methods: A recursive tree-based algorithm that can consider multiple object fragments simultaneously is described. Starting with oversegmented data, it searches efficiently for the optimal merging pattern guided by a quantitative scoring criterion based on object modeling. Computation is bounded by limiting the depth of the merging tree.

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-

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Anatomical connectivity and lesion studies reveal distinct functional heterogeneity along the dorsal-ventral axis of the hippocampus. The immediate early gene Arc is known to be involved in neural plasticity and memory and can be used as a marker for cell activity that occurs, for example, when hippocampal place cells fire. We report here, that Arc is expressed in a greater proportion of cells in dorsal CA1, CA3, and dentate gyrus (DG), following spatial behavioral experiences compared to ventral hippocampal subregions (dorsal CA1 = 33%; ventral CA1 = 13%; dorsal CA3 = 23%; ventral CA3 = 8%; and dorsal DG = 2.5%; ventral DG = 1.2%). The technique used here to obtain estimates of numbers of behavior-driven cells across the dorsal-ventral axis, however, corresponds quite well with samples from available single unit recording studies. Several explanations for the two- to-threefold reduction in spatial behavior-driven cell activity in the ventral hippocampus can be offered. These include anatomical connectivity differences, differential gain of the self-motion signals that appear to alter the scale of place fields and the proportion of active cells, and possibly variations in the neuronal responses to non-spatial information within the hippocampus along its dorso-ventral axis.