Dataset Information

Traxtile: Interactive editing of cell tracks in time-lapse images.

ABSTRACT: Time-lapse imaging can be used to quantify how cells move, divide, and die over time and under defined culture conditions. Open source software packages such as CellProfiler, Icy, and Fiji provide robust and convenient interfaces for performing such analyses. However, object tracking algorithms are imperfect, and validation of significant events is often required. This is challenging, as CellProfiler produces only tabular data for object tracking, and the graphical tools in Icy and Fiji are not optimal for manual review of these events. Here we describe Traxtile, a program that allows interactive graphical review and revision of object tracking assignments. Traxtile imports initial assignments and automatically identifies events needing review (i.e., apparent creation of new objects, splits, merges, and losses). For each such event, the object track is displayed on a montage of images centered on the event and spanning the preceding and subsequent frames. Links between cells in successive frames can be reviewed and edited, yielding validated tracks for the image series. Reports summarize events from the validated tracks. Traxtile is implemented in Python version 2.7 using standard distribution libraries (available at www.python.org) and is freely available at https://github.com/braunb/traxtile-public.

SUBMITTER: Braun BS

PROVIDER: S-EPMC4560953 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Traxtile: Interactive editing of cell tracks in time-lapse images.

Braun Benjamin S BS

BioTechniques 20150801 2

Time-lapse imaging can be used to quantify how cells move, divide, and die over time and under defined culture conditions. Open source software packages such as CellProfiler, Icy, and Fiji provide robust and convenient interfaces for performing such analyses. However, object tracking algorithms are imperfect, and validation of significant events is often required. This is challenging, as CellProfiler produces only tabular data for object tracking, and the graphical tools in Icy and Fiji are not ...[more]

PMID: 26260086

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Project description:BackgroundRecently a series of algorithms have been developed, providing automatic tools for tracing C. elegans embryonic cell lineage. In these algorithms, 3D images collected from a confocal laser scanning microscope were processed, the output of which is cell lineage with cell division history and cell positions with time. However, current image segmentation algorithms suffer from high error rate especially after 350-cell stage because of low signal-noise ratio as well as low resolution along the Z axis (0.5-1 microns). As a result, correction of the errors becomes a huge burden. These errors are mainly produced in the segmentation of nuclei. Thus development of a more accurate image segmentation algorithm will alleviate the hurdle for automated analysis of cell lineage.ResultsThis paper presents a new type of nuclei segmentation method embracing an bi-directional prediction procedure, which can greatly reduce the number of false negative errors, the most common errors in the previous segmentation. In this method, we first use a 2D region growing technique together with the level-set method to generate accurate 2D slices. Then a modified gradient method instead of the existing 3D local maximum method is adopted to detect all the 2D slices located in the nuclei center, each of which corresponds to one nucleus. Finally, the bi-directional prediction method based on the images before and after the current time point is introduced into the system to predict the nuclei in low quality parts of the images. The result of our method shows a notable improvement in the accuracy rate. For each nucleus, its precise location, volume and gene expression value (gray value) is also obtained, all of which will be useful in further downstream analyses.ConclusionsThe result of this research demonstrates the advantages of the bi-directional prediction method in the nuclei segmentation over that of StarryNite/MatLab StarryNite. Several other modifications adopted in our nuclei segmentation system are also discussed.

Dataset Information

Traxtile: Interactive editing of cell tracks in time-lapse images.

Publications

Traxtile: Interactive editing of cell tracks in time-lapse images.

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