Smart 3D‐fish: Automation of distance analysis in nuclei of interphase cells by image processing

Abstract

Background: Detection of fluorescent probes by fluorescence in situ hybridization in cells with preserved threedimensional nuclear structures (3D-FISH) is useful for studying the organization of chromatin and localization of genes in interphase nuclei. Fast and reliable measurements of the relative positioning of fluorescent spots specific to subchromosomal regions and genes would improve understanding of cell structure and function. Methods: 3D-FISH protocol, confocal microscopy, and digital image analysis were used. Results: New software (Smart 3D-FISH) has been developed to automate the process of spot segmentation and distance measurements in images from 3D-FISH experiments. It can handle any number of fluorescent spots and incorporate images of 4 0 ,6-diamino-2-phenylindole counterstained nuclei to measure the relative positioning of

State‐of‐the‐art FISHing: Automated analysis of cytogenetic aberrations in interphase nuclei

“…To detect FISH spots, a 320 objective might be appropriate (28,29); however, if not only the number and intensity of signals are important but their spatial pattern as well, the application of a higher magnification and more importantly high resolution is desirable (30)(31)(32). Considering the prior, several workgroups use 360, 363, or 3100 objectives during the automated i-FISH analysis (21,(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43). An alternative approach, also increasing speed, is the use of two objectives, one of them for nucleus selection (objective magnification: and the other one for detecting FISH signals (objective magnification: 340-100) (31,44-52).…”

“…While the technological and computational development continued with undiminished energy during the following years (22,35,38,41,44,46,60,61,66,(81)(82)(83)(84)(85)(86)(87)(88)(89)(90)(91)(92)(93), even more emphasis was laid on practical approaches and a broad spectrum of clinical applications appeared in the beginning of the 21st century.…”

Image Processing Methods for Automatic Cell Counting In Vivo or In Situ Using 3D Confocal Microscopy

“…3D image processing techniques can be used to improve the quality of segmentation. This is important when the signal to noise ratio is low, given that some spots can be considered noise in a 2D image, but recognized as true particles in 3D (Gué, 2005). To work in 3D, other techniques should be considered before filtering.…”

A Two-Phase Segmentation of Cell Nuclei Using Fast Level Set-Like Algorithms

“…The most commonly used cell nucleus segmentation algorithms are based on thresholding [3,4] and region-growing [5,6] approaches. Their main advantage consists in the automation of the entire segmentation process.…”