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

Background: To 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. Results: We 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 reconstru...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Anthony Santella, Zhuo Du, Sonja Nowotschin, Anna-Katerina Hadjantonakis, Zhirong Bao
• Keywords: BMCBI 2010, Business, Sectioned, Shape Model, Single Cell