Automatic detection of clustered, fluorescent‐stained nuclei by digital image‐based cytometry

Abstract

Automatic image-based cytometry (IC) can conveniently quantify the distributions of several specific, fluorescencelabeled molecules within individual, isolated cells of slide-or tissue-based specimens. However, many specimens contain clusters of cells or nuclei that are not detected as individual entities by existing automatic methods. We have developed analysis algorithms which detect individual nuclei occurring in clusters or as isolated nuclei. Specimens were labeled with a fluorescent DNA stain, imaged and the images were segmented into regions of nuclei and background. Clusters of nuclei, identified by their size and shape, were divided into individual nuclei by searching for dividing paths between nuclei. The paths, which need not be straight, possessed the highest average gradient per pixel. In addition, both high-and low-pass filtered images of the original image were analyzed. For each individual nucleus, one of the three seg mented regions representing the nucleus (from either the original or one of two filtered images) was chosen as the final result, based on the closeness of the regions to average nuclear morphology. The algorithms correctly detected a high proportion of isolated (328/333) and clustered (2541271) nuclei when applied to images of 2 p.m prostate and breast cancer sections. Thus, these algorithms should enable much more accurate detection and analyses of nuclei in intact specimens. Key terms: Image analysis, image cytom etry, nuclei, tissue sections Digital image-based cytometry (IC) is a technique which analyses slide-based specimens using a microscope coupled to an electronic camera and computer. The images are stored digitally in computer memory from where they are accessible for analysis by software algorithms. IC is now a primary technique for understanding normal and pathological cellular mechanisms, because it is possible to quantitatively and nondestructively measure wide ranges of biochemical, morphological, densitometric, and contextual parameters on the individual cells and nuclei in the specimens (2,10,21,23,24,25,29,38,40).The convenient measurement of many individual nuclei requires image analysis algorithms that automatically locate every nucleus. This i s usually accomplished by staining the nuclei in such a way that their corresponding image intensities are significantly different from the background intensities. Then, an algorithm that calculates threshold intensities between nuclear and background intensities, can be used to segment the nuclei from the background (26). In our IC (10,24), specimens are stained with a fluorescent DNA dye, because all nuclei contain abundant DNA and are thus represented in the images by high pixel intensities against a low intensity background. Images are automatically segmented into regions representing nuclei and background. This method, and others (27), correctly detects almost 100% of isolated nuclei (24).In most specimens, particularly clinical specimens, there are clustered cells where different nuclei appear touching or overlappin...

A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks

“…Segmentation of intact cell nuclei from 3D confocal microscope images is an essential capability for numerous hypothesis testing studies, especially where knowledge of morphology of cell nuclei, the distribution of fluorescence signals associated with them, and/or the organization of cells in the tissue specimen is required (1,64,65). Figure 1 illustrates 3D nucleus segmentation.…”

“…Region-based approaches, such as thresholding and labeling, are only suitable for images containing well-isolated objects. Other techniques, such as splitting and merging (11)(12)(13)(14), simple region growing (15), multiple thresholding (16), and direct morphological segmentation techniques (17)(18)(19)(20)(21)(22), did not produce good results, especially when the images contained a high density of cell nuclei, and each cell exhibited significant variation of size, shape, and intensity. The need to process large batches of 3D images (50 -100 images per batch) at interactive computer speeds has influenced this work significantly, as will be noted throughout the article.…”

Whole cell segmentation in solid tissue sections

“…Previous work on analyzing microscope images of fluorescence-labeled tissue samples has predominantly focused on segmenting cell nuclei (1)(2)(3)(4), and there are only a few reports of methods for segmenting whole cells (i.e., the cell nucleus plus the surrounding cytoplasm) (5-7). Segmentation of whole cells in intact tissue introduces additional problems compared with isolated cells in culture, because the cells are in a close packing arrangement; in other words, they are in complete contact with their neighbors.…”

High content image cytometry in the context of subnuclear organization

“…To anticipate for erroneous segmentation of clustered nuclei in dense cell cultures, we implemented an iterative conditional segmentation algorithm (ICS) that uses both morphological and intensity information from the image. In analogy with proposed nuclei segmentation methods for tissue sections (26,27), this method makes use of a priori knowledge about the size and shape of nuclei in stringent feedback (de-) selection of (in-) correctly segmented nuclei. Subsequent analysis of subnuclear features is optimized to cater for a broad range of objects, such as DNA-loci, DNA damage repair spot or proteinaceous bodies, by means of multiscale techniques and adaptive segmentation.…”