A hybrid blob-slice model for accurate and efficient detection of fluorescence labeled nuclei in 3D

BMC Bioinformatics

Methodology Article

Sonja Nowotschin¹  Zhirong Bao¹  Zhuo Du¹  Anthony Santella¹  Anna-Katerina Hadjantonakis¹ 

[1] Developmental Biology, Sloan-Kettering Institute, 1275 York Avenue, 10065, New York, New York, USA;

关键词: Shape Model;    Nuclear Shape;    Nuclear Detection;    High Throughput Imaging;    Nuclear Separation;   

DOI  :  10.1186/1471-2105-11-580

 received in 2010-06-16, accepted in 2010-11-29,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundTo exploit the flood of data from advances in high throughput imaging of optically sectioned nuclei, image analysis methods need to correctly detect thousands of nuclei, ideally in real time. Variability in nuclear appearance and undersampled volumetric data make this a challenge.ResultsWe present a novel 3D nuclear identification method, which subdivides the problem, first segmenting nuclear slices within each 2D image plane, then using a shape model to assemble these slices into 3D nuclei. This hybrid 2D/3D approach allows accurate accounting for nuclear shape but exploits the clear 2D nuclear boundaries that are present in sectional slices to avoid the computational burden of fitting a complex shape model to volume data. When tested over C. elegans, Drosophila, zebrafish and mouse data, our method yielded 0 to 3.7% error, up to six times more accurate as well as being 30 times faster than published performances. We demonstrate our method's potential by reconstructing the morphogenesis of the C. elegans pharynx. This is an important and much studied developmental process that could not previously be followed at this single cell level of detail.ConclusionsBecause our approach is specialized for the characteristics of optically sectioned nuclear images, it can achieve superior accuracy in significantly less time than other approaches. Both of these characteristics are necessary for practical analysis of overwhelmingly large data sets where processing must be scalable to hundreds of thousands of cells and where the time cost of manual error correction makes it impossible to use data with high error rates. Our approach is fast, accurate, available as open source software and its learned shape model is easy to retrain. As our pharynx development example shows, these characteristics make single cell analysis relatively easy and will enable novel experimental methods utilizing complex data sets.

【 授权许可】

Unknown   
© Santella et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.