Abstract
Cell tracking is one of the most critical tools for time-lapse image analysis to observe cell behavior and cell lineages over a long period of time. However, the accompanying graphical user interfaces are often difficult to use and do not incorporate seamless manual correction, data analysis tools, or simple training set design tools if it is machine learning based. In this paper, we introduce our cell tracking software “LIM Tracker”. This software has a conventional tracking function consisting of recognition processing and link processing, a sequential search-type tracking function based on pattern matching, and a manual tracking function. LIM Tracker enables the seamless use of these functions. In addition, the system incorporates a highly interactive and interlocking data visualization method, which displays analysis result in real time, making it possible to flexibly correct the data and reduce the burden of tracking work. Moreover, recognition functions with deep learning (DL) are also available, which can be used for a wide range of targets including stain-free images. LIM Tracker allows researchers to track living objects with good usability and high versatility for various targets. We present a tracking case study based on fluorescence microscopy images (NRK-52E/EKAREV-NLS cells or MCF-10A/H2B-iRFP-P2A-mScarlet-I-hGem-P2A-PIP-NLS-mNeonGreen cells) and phase contrast microscopy images (Glioblastoma-astrocytoma U373 cells). LIM Tracker is implemented as a plugin for ImageJ/Fiji. The software can be downloaded from https://github.com/LIMT34/LIM-Tracker.
Highlights
Cell tracking is one of the most critical tools for time-lapse image analysis to observe cell behavior and cell lineages over a long period of time
Editing trajectories can only be done on the cell lineage screen (TrackScheme), but it is difficult to determine the correctness of the trajectory from the lineage diagram alone, and the operation is complicated, especially when the number of targets increases
The cDNA was further subcloned into a pCSII-based lentiviral vector[17], pCSIIpuro-MCS, generating pCSIIpuro-Histone 2B (H2B)-iRFP-P2A-mScarlet-I-human Geminin (hGem)-P2A-PIP-tag-NLS-mNeonGreen. psPAX2 was a gift from Dr D
Summary
Cell tracking is one of the most critical tools for time-lapse image analysis to observe cell behavior and cell lineages over a long period of time. Usiigaci has a powerful DL recognition function that can handle a variety of targets including stain-free images Both of them lack usability because they cannot be operated with a graphical user interface that enables effective interaction with the user, and they do not provide a means to check the validity of processing results or to correct them. The former requires users to have specialized knowledge of image processing and analysis, while the latter is difficult to use unless the user has a certain level of programming knowledge
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