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

• Published in: BMC bioinformatics Vol. 15; no. 1; p. 9
• Main Authors: Gul-Mohammed, Jaza, Arganda-Carreras, Ignacio, Andrey, Philippe, Galy, Vincent, Boudier, Thomas
• Format: Journal Article
• Language: English
• Published: London BioMed Central 14.01.2014; BioMed Central Ltd; Springer Nature B.V
• Subjects: Algorithms; Animals; Automation; Bioinformatics; Biomedical and Life Sciences; Caenorhabditis elegans; Caenorhabditis elegans - embryology; Cell Nucleus - ultrastructure; Classification; Computational Biology; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Databases, Factual; Drosophila - embryology; Embryo, Nonmammalian - ultrastructure; Embryonic growth stage; Embryos; image analysis and data visualization; Image processing; Imaging; Imaging, Three-Dimensional - methods; Life Sciences; Methods; Microarrays; Models, Genetic; Nematodes; Physiological aspects; Research Article; Studies; Training; 4D; Development; Image segmentation; 3D; Classification; Cell cycle; C. elegans; development; drosophila; image segmentation; cell cycle; classification
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-15-9

Background 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. Results 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. Conclusion We developed a novel generic algorithm for segmentation and classification in 3D images. The method, referred to as A daptive G eneric I terative T hresholding A lgorithm (AGITA), is freely available as an ImageJ plug-in.