Application and Quantitative Validation of Computer-Automated Three-Dimensional Counting of Cell Nuclei.

: This study provides a quantitative validation of qualitative automated three-dimensional (3-D) analysis methods reported earlier. It demonstrates the applicability and quantitative accuracy of our method to detect, characterize, and count Feulgen stained cell nuclei in two tissues (hippocampus and testes). These methods can provide important insights into the interpretation of biological, pharmacological, pathological, and toxicological events. A laser-scanned confocal light microscope was used to record 3-D images in which our algorithms automatically identified individual nuclei from the optical sections given an estimate of minimum nuclear size. The hippocampal data sets were also manually counted independently by five trained observers using the STERECON 3-D image reconstruction system. The automated and manual counts were compared. The computer counts were lower ( approximately 14%) than the manual counts, mainly because the algorithms counted a nucleus only if it was present in five consecutive optical sections but the human counters included nuclei that were in fewer optical sections. A nucleus-by-nucleus comparison of the manual and automated counts verified that the automated analysis was accurate and reproducible, and permitted additional quantitative analyses not available from manual methods. The algorithms also identified subpopulations of nuclei within the hippocampal samples, and haploid and diploid nuclei in the testes. Our methods were shown to be repeatable, accurate, and more consistent than manual counting. Nuclei in regions of high (hippocampal pyramidal layer) and low (extrapyramidal layer) density were distinguished with equal ease. Haploid and diploid nuclei were distinguished in the testes, demonstrating that our automated method may be useful for ploidy analysis. The results presented here on hippocampus and testis are consistent with other qualitative results from the liver and from immunohistochemically labeled substantia nigra, demonstrating the applicability of our software across tissues and preparation methods.

Advances in automated 3-D image analyses of cell populations imaged by confocal microscopy.

Automated three-dimensional (3-D) image analysis methods are presented for rapid and effective analysis of populations of fluorescently labeled cells or nuclei in thick tissue sections that have been imaged three dimensionally using a confocal microscope. The methods presented here greatly improve upon our earlier work (Roysam et al.:J Microsc 173: 115-126, 1994). The principal advances reported are: algorithms for efficient data pre-processing and adaptive segmentation, effective handling of image anisotrophy, and fast 3-D morphological algorithms for separating overlapping or connected clusters utilizing image gradient information whenever available. A particular feature of this method is its ability to separate densely packed and connected clusters of cell nuclei. Some of the challenges overcome in this work include the efficient and effective handling of imaging noise, anisotrophy, and large variations in image parameters such as intensity, object size, and shape. The method is able to handle significant inter-cell, intra-cell, inter-image, and intra-image variations. Studies indicate that this method is rapid, robust, and adaptable. Examples were presented to illustrate the applicability of this approach to analyzing images of nuclei from densely packed regions in thick sections of rat liver, and brain that were labeled with a fluorescent Schiff reagent.

Automated 3-D montage synthesis from laser-scanning confocal images: application to quantitative tissue-level cytological analysis.

This paper presents a landmark based method for efficient, robust, and automated computational synthesis of high-resolution, two-dimensional (2-D) or three-dimensional (3-D) wide-area images of a specimen from a series of overlapping partial views. The synthesized image is the set union of the areas or volumes covered by the partial views, and is called the "montage." This technique is used not only to produce gray-level montages, but also to montage the results of automated image analysis, such as 3-D cell segmentation and counting, so as to generate large representations that are equivalent to processing the large wide-area image at high resolution. The method is based on computing a concise set of feature-tagged landmarks in each partial view, and establishing correspondences between the landmarks using a combinatorial point matching algorithm. This algorithm yields a spatial transformation linking the partial views that can be used to create the montage. Such processing can be a first step towards high-resolution large-scale quantitative tissue studies. A detailed example using 3-D laser-scanning confocal microscope images of acriflavine-stained hippocampal sections of rat brain is presented to illustrate the method.

Three-dimensional imaging and image analysis of hippocampal neurons: confocal and digitally enhanced wide field microscopy.

The microscopy of biological specimens has traditionally been a two-dimensional imaging method for analyzing what are in reality three-dimensional (3-D) objects. This has been a major limitation of the application of one of science's most widely used tools. Nowhere has this limitation been more acute than in neurobiology, which is dominated by the necessity of understanding both large- and small-scale 3-D anatomy. Fortunately, recent advances in optical instrumentation and computational methods have provided the means for retrieving the third dimension, making full 3-D microscopic imaging possible. Optical designs have concentrated on the confocal imaging mode while computational methods have made 3-D imaging possible with wide field microscopes using deconvolution methods. This work presents a brief review of these methods, especially as applied to neurobiology, and data using both approaches. Specimens several hundred micrometers thick can be sampled allowing essentially intact neurons to be imaged. These neurons or selected components can be contrasted with either fluorescent, absorption, or reflection stains. Image analysis in 3-D is as important as visualization in 3-D. Automated methods of cell counting and analysis by nuclear detection as well as tracing of individual neurons are presented.

Algorithms for automated characterization of cell populations in thick specimens from 3-D confocal fluorescence microscopy data.

Methods are presented for the automated, quantitative and three-dimensional (3-D) analysis of cell populations in thick, essentially intact tissue sections while maintaining intercell spatial relationships. This analysis replaces current manual methods which are tedious and subjective. The thick sample is imaged in three dimensions using a confocal scanning laser microscope. The stack of optical slices is processed by a 3-D segmentation algorithm that separates touching and overlapping structures using localization constraints. Adaptive data reduction is used to achieve computational efficiency. A hierarchical cluster analysis algorithm is used automatically to characterize the cell population by a variety of cell features. It allows automatic detection and characterization of patterns such as the 3-D spatial clustering of cells, and the relative distributions of cells of various sizes. It also permits the detection of structures that are much smaller, larger, brighter, darker, or differently shaped than the rest of the population. The overall method is demonstrated for a set of rat brain tissue sections that were labelled for tyrosine hydroxylase using fluorescein-conjugated antibodies. The automated system was verified by comparison with computer-assisted manual counts from the same image fields.