Efficient, interactive, and three-dimensional segmentation of cell nuclei in thick tissue sections

Abstract

Segmentation of intact cell nuclei in three‐dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. Because automatic algorithms do not correctly segment all nuclei in tissue sections, interactive algorithms may be preferable for some applications. Existing interactive segmentation algorithms require the analyst to draw a border around the nucleus under consideration in all successive two‐dimensional (2D) planes of the 3D image. The present paper describes an algorithm with two main advantages over the existing method. First, the analyst draws borders only in 2D planes that cut approximately through the center of the nucleus under consideration so that the nuclear borders generally are most distinct. Second, the analyst draws only five borders around each nucleus, and then the algorithm interpolates the entire surface. The algorithm results in segmented objects that correspond to individual, visually identifiable nuclei. The segmented surfaces, however, may not exactly represent the true nuclear surface. An optional, automatic surface optimization algorithm can be applied to reduce this error. Cytometry 31:275–286, 1998. © 1998 Wiley‐Liss, Inc.

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

“…The former is based on manually delineating nuclei in sequential and orthogonal 2D slices using a computer graphics device such as a tablet or mouse, relying on the human visual system and expert judgment (2,3,4). Sometimes, enhanced 3D visualization capabilities are employed (56 -58).…”

Multinucleation of koilocytes is in fact multilobation and is related to aberration of the G2 checkpoint

“…[21][22][23][24][25][26] With the use of a DNA PI probe in combination with an anticytokeratin antibody, it has been shown that the nuclear shapes of dysmorphic cells can be clearly visualised in three dimensions. First, viewing a series of confocal images visually identifies the cell of interest, such as a koilocyte.…”

Cell Image Processing Methods for Automatic Cell Pattern Recognition and Morphological Analysis of Mesenchymal Stem Cells - An Algorithm for Cell Classification and Adaptive Brightness Correction -

“…Other researchers have recently developed and suggested the method for quantifying cell formation associated with green fluorescent protein (GFP)-Rac1 translocation in individual cells. This image of the cell nuclei was first segmented, and the result was used as seeds to simplify the segmentation of the cytoplasms (Lockett et al, 1998) Combining shape and intensity with the use of morphological filtering for good solution has some problems when segmenting bright-field microscopic images of cells. In order to track cultured cells as they differentiate to neurons and glial cells, we need a fast and robust segmentation algorithm.…”