Vertebrate neural stem cell segmentation, tracking and lineaging with validation and editing

Winter, Mark; Wait, Eric; Roysam, Badrinath; Goderie, Susan K.; Ali, Rania Ahmed Naguib; Kokovay, Erzsebet; Temple, Sally; Cohen, Andrew R.

doi:10.1038/nprot.2011.422

Cited by 55 publications

(99 citation statements)

References 15 publications

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“…These methods use either frame-frame segmentation and association [1], or motion filter tracking [2]. More recently, methods were proposed for predicting the fate of the observed cells [3], [4]. Such methods use features extraction and classification methods to predict the fate of a cell, like 1 S. Rigaud death, division, or dividing into a specialised cell.…”

Section: Introductionmentioning

confidence: 99%

An analysis-synthesis approach for neurosphere modelisation under phase-contrast microscopy

Rigaud¹,

Huang²,

Ahmed³

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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The study of stem cells is one of the most important biomedical research. Understanding their development could allow multiple applications in regenerative medicine. For this purpose, automated solutions for the observation of stem cell development process are needed. This study introduces an on-line analysis method for the modelling of neurosphere evolution during the early time of their development under phase contrast microscopy. From the corresponding phase contrast time-lapse sequences, we extract information from the neurosphere using a combination of phase contrast physics deconvolution and curve detection for locate the cells inside the neurosphere. Then, based on prior biological knowledge, we generate possible and optimal 3-dimensional configuration using 2D to 3D registration methods and evolutionary optimisation algorithm.

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Section: Introductionmentioning

confidence: 99%

An analysis-synthesis approach for neurosphere modelisation under phase-contrast microscopy

Rigaud¹,

Huang²,

Ahmed³

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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show abstract

“…Previously, we developed computational tools for the analysis of 2D time lapse image sequences showing the proliferation of a clone , or family tree, of cells from a single progenitor through the ultimate differentiated progeny [2]. Recently, we have extended these computational tools to incorporate 3D visualization and analysis components [3].…”

Section: Introductionmentioning

confidence: 99%

Multisensory interface for 5D stem cell image volumes

Koerner¹,

Wait

Winter

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Biological imaging of live cell and tissue using 3D microscopy is able to capture time-lapse image sequences showing multiple molecular markers labeling different biological structures simultaneously. In order to analyze this complex multi-dimensional image sequence data, there is a need for automated quantitative algorithms, and for methods to visualize and interact with both the data and the analytical results. Traditional computational human input devices such as the keyboard and mouse are no longer adequate for complex tasks such as manipulating and navigating 3+ dimensional volumes. In this paper, we have developed a new interaction system for interfacing with big data sets using the human visual system together with touch, force and audio feedback. This system includes real-time dynamic 3D visualization, haptic interaction via exoskeletal glove, and tonal auditory components that seamlessly create an immersive environment for efficient qualitative analysis.

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“…Each image is first segmented, and then tracked using the approach previously described for the LEVER (lineage editing and validation) software tools [54][55][56][57] . The segmentation processed the two FUCCI channels using a denoising algorithm 58 that models imaging noise not peer-reviewed) is the author/funder.…”

Section: Computational Image Analysismentioning

confidence: 99%

Directed, but not random, breast cancer cell migration is faster in the G1 phase of the cell cycle in 2D and 3D environments

et al. 2018

Preprint

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Cancer cell migration is essential for the early steps of metastasis, during which cancer cells move through the primary tumor and reach the blood vessels. In vivo, cancer cells are exposed to directional guidance cues, either soluble, such as gradients of growth factors, or insoluble, such as collagen fiber alignment. Depending on the number and strength of such cues, cells will migrate in a random or directed manner. Interestingly, similar cues also stimulate cell proliferation. In this regard, it is not clear whether cell cycle progression affects migration of cancer cells and whether this effect is different in random versus directed migration. In this study, we tested the effect of cell cycle progression on random and directed migration, both in 2D and 3D environments, in the breast carcinoma cell line, FUCCI-MDA-MB-231, using computational image analysis by LEVER. Directed migration in 2D was modeled as chemotaxis along a gradient of soluble EGF inside 10 µm-wide microchannels. In 3D, directed migration was modeled as contact guidance (alignotaxis) along aligned collagen fibers. Time-lapse recordings of cells in 2D and 3D revealed that directed, but not random migration, is cell cycle-dependent. In both 2D and 3D directed migration, cells in the G1 phase of the cell cycle outperformed cells in the G2 phase in terms of migration persistence and instantaneous velocity. These data suggest that in the presence of guidance cues in vivo, breast carcinoma cells in the G1 phase of the cell cycle may be more efficient in reaching vasculature.not peer-reviewed)

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Vertebrate neural stem cell segmentation, tracking and lineaging with validation and editing

Cited by 55 publications

References 15 publications

An analysis-synthesis approach for neurosphere modelisation under phase-contrast microscopy

An analysis-synthesis approach for neurosphere modelisation under phase-contrast microscopy

Multisensory interface for 5D stem cell image volumes

Directed, but not random, breast cancer cell migration is faster in the G1 phase of the cell cycle in 2D and 3D environments

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