Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences

BMC Bioinformatics

Eric Wait³  Mark Winter³  Chris Bjornsson¹  Erzsebet Kokovay²  Yue Wang¹  Susan Goderie¹  Sally Temple¹  Andrew R Cohen³ 

[1] Neural Stem Cell Institute, 12144 Rensselaer, USA

[2] UT Health Science Center, 78229 San Antonio, USA

[3] Drexel University, 19104 Philadelphia, USA

关键词: Image montage reconstruction;    CUDA;    Confocal microscopy;    Validation and correction;    Lineaging;    Time lapse;    Stem cell;    Stereoscopic 3-D;    3-D display;    Volumetric texture rendering;   

Others  :  1085595
DOI  :  10.1186/1471-2105-15-328

 received in 2014-04-22, accepted in 2014-09-19,  发布年份 2014

【 摘 要 】

Background

Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate.

Results

We present an application that integrates visualization and quantitative analysis of 5-D (x,y,z,t,channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks.

Conclusions

By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. We combine unsupervised image analysis algorithms with an interactive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.

【 授权许可】

   
2014 Wait et al.; licensee BioMed Central Ltd.

【 图 表 】

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