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

Abstract

BackgroundNeural 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.ResultsWe 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.ConclusionsBy 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.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-328) contains supplementary material, which is available to authorized users.

Automated Measurement of Cobblestone Morphology for Characterizing Stem Cell Derived Retinal Pigment Epithelial Cell Cultures

“…Alternative approaches to characterizing RPE development might benefit from time-lapse microscopy analysis, 17,[21][22][23] where cobblestone structure formation can be visualized over time. Time-lapse microscopy requires more complex imaging and analysis, making it less well-suited for practical characterization of RPE cultures.…”

“…17 The approach models image noise as consisting of low-frequency structural noise and high-frequency imaging shot noise. A Gaussian filter with standard deviation of 10 pixels is used to remove low-frequency structural background.…”

“…We use an approach applied previously in the analysis of 3D timelapse microscopy of neural stem cells 17 known as contrast enhancement filtering. 18 Contrast enhancement filtering models the imaging noise as consisting of slow varying structural background noise combined with high-frequency shot noise.…”

Multisensory interface for 5D stem cell image volumes

“…Recently, we have extended these computational tools to incorporate 3D visualization and analysis components [3]. This stem cell image analysis and visualization software allows these five dimensional data sets to be effectively visualized.…”

Measuring and Visualizing Clonal Development in Live Cell and Tissue Microscopy

“…The LEVER (Lineage Editing and Validation) software tools are an open-source NIH funded application suite designed to enable efficient segmentation, tracking and lineaging of 5-D time-lapse microscopy image sequences [2][3][4]. LEVER is able to process hundreds of movies with thousands of cells and millions of segmentations and works with both phase and fluorescence microscopy.…”