Segmentation of occluded hematopoietic stem cells from tracking

Abstract

Image sequences of live proliferating cells often contain visual ambiguities that are difficult even for human domain experts to resolve. Here we present a new approach to analyzing image sequences that capture the development of clones of hematopoietic stem cells (HSCs) from live cell time lapse microscopy. The HSCs cannot survive long term imaging unless they are cultured together with a secondary cell type, OP9 stromal cells. The HSCs frequently disappear under the OP9 cell layer, making segmentation difficult or impossible from a single image frame, even for a human domain expert. We have developed a new approach to the segmentation of HSCs that captures these occluded cells. Starting with an a priori segmentation that uses a Monte Carlo technique to estimate the number of cells in a clump of touching cells, we proceed to track and lineage the image data. Following user validation of the lineage information, an a posteriori resegmentation step utilizing tracking results delineates the HSCs occluded by the OP9 layer. Resegmentation has been applied to 3031 occluded segmentations from 77 tracks, correctly recovering over 84% of the occluded segmentations.

Computational Image Analysis Reveals Intrinsic Multigenerational Differences between Anterior and Posterior Cerebral Cortex Neural Progenitor Cells

“…Image data gathered in three separate experiments was initially segmented, tracked, and lineaged, according to the process outlined in Figure 1 . These initial segmentation and tracking algorithms have been applied in a number of recent applications ( Chenouard et al., 2014 , Cohen et al., 2009 , Cohen et al., 2010 , Mankowski et al., 2014 , Winter et al., 2011 , Winter et al., 2012 ). We developed a new segmentation algorithm that uses lineage information to automatically improve segmentation and tracking accuracy in a step referred to as “post-lineage refinement”.…”

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

“…Most of these errors were due to the cells not separating immediately after mitosis. We recently developed improved techniques for resolving visual ambiguity in 2-D image sequences of proliferating cells by incorporating tracking and lineaging information [ 19 ]. These methods offer a promising approach to reduce the number of errors in the 3-D segmentation and will be added in future versions.…”

Time-Lapse Microscopy

“…By now, various algorithms are designed for quantifying and tracking cell migration [3] and Time-Lapse Microscopy http://dx.doi.org/10.5772/intechopen.81199 single cell motility [261,263]; cell proliferation [264]; cell cycle and cell lineage analysis [107]; changes in mitotic and interphase duration [141]; cell-cell contacts [52]; studying specific cells and tissues [265] and specific intracellular processes such as transcription [99] or morphogenesis [266]; colocalization of cells and intracellular markers [184]; tracking cellular organelles [258]; highlighting the certain cell type within tissues or mixed cell cultures [267]; clustered, overlapping or dying cells [268]; in toto imaging of developing organisms, tissues and organs [241] and assessing development and selection of embryos for in vitro fertilization [269,270].…”