Open Access Open Badges Research article

A generic classification-based method for segmentation of nuclei in 3D images of early embryos

Jaza Gul-Mohammed15, Ignacio Arganda-Carreras34, Philippe Andrey134, Vincent Galy12 and Thomas Boudier1*

Author Affiliations

1 Sorbonne Universités, UPMC Univ Paris 06, 4 place Jussieu, 75005 Paris, France

2 CNRS-UMR7622, Université Pierre et Marie Curie, 4 place Jussieu, 75005 Paris, France

3 INRA, UMR1318, Institut Jean-Pierre Bourgin, 78026 Versailles, France

4 AgroParisTech, UMR1318, Institut Jean-Pierre Bourgin, 78026 Versailles, France

5 University of Sulaimani, School of Engineering, 46001 Sulaimani, Iraq

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BMC Bioinformatics 2014, 15:9  doi:10.1186/1471-2105-15-9

Published: 14 January 2014



Studying how individual cells spatially and temporally organize within the embryo is a fundamental issue in modern developmental biology to better understand the first stages of embryogenesis. In order to perform high-throughput analyses in three-dimensional microscopic images, it is essential to be able to automatically segment, classify and track cell nuclei. Many 3D/4D segmentation and tracking algorithms have been reported in the literature. Most of them are specific to particular models or acquisition systems and often require the fine tuning of parameters.


We present a new automatic algorithm to segment and simultaneously classify cell nuclei in 3D/4D images. Segmentation relies on training samples that are interactively provided by the user and on an iterative thresholding process. This algorithm can correctly segment nuclei even when they are touching, and remains effective under temporal and spatial intensity variations. The segmentation is coupled to a classification of nuclei according to cell cycle phases, allowing biologists to quantify the effect of genetic perturbations and drug treatments. Robust 3D geometrical shape descriptors are used as training features for classification. Segmentation and classification results of three complete datasets are presented. In our working dataset of the Caenorhabditis elegans embryo, only 21 nuclei out of 3,585 were not detected, the overall F-score for segmentation reached 0.99, and more than 95% of the nuclei were classified in the correct cell cycle phase. No merging of nuclei was found.


We developed a novel generic algorithm for segmentation and classification in 3D images. The method, referred to as Adaptive Generic Iterative Thresholding Algorithm (AGITA), is freely available as an ImageJ plug-in.

Image segmentation; Classification; Cell cycle; 3D; 4D; C. elegans; Drosophila; Development