Evidence map›Paper›PMID 42040257›Full record

ArticleComputational and structural biotechnology journal2026

Tui: A Multigenerational and Expert-Correctable Tracker for Cellular Dynamics.

I-Ming Chang, Hsieh-Fu Tsai

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

I-Ming ChangDepartment of Biomedical Engineering, Chang Gung University, Taoyuan 333, Taiwan.ORCID https://orcid.org/0009-0004-6359-4923
Hsieh-Fu TsaiDepartment of Biomedical Engineering, Chang Gung University, Taoyuan 333, Taiwan.ORCID https://orcid.org/0000-0002-8652-4643

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell division, migration, and collective cell behaviors, along with less frequent events such as cell fusion, are fundamental biological processes underlying physiological and pathological mechanisms. However, accurate cell tracking and lineage reconstruction from time-lapse microscopy remain challenging because of failure modes such as identity switching and division misassignment, arising from dynamic cell behavior, morphological heterogeneity, and intermittent segmentation errors. We introduce Tui tracker, a multigenerational tracking framework that integrates integer linear programming optimization with lineage-aware corrections for both cell division and fusion. Although fully automated, the Tui tracker includes an expert-correctable graphical interface, allowing users to optionally inspect and refine lineage trees. We validate the Tui tracker using synthetic datasets and real-world data, including the DIC-C2DH-HeLa and Fluo-N2DH-SIM+ datasets from the Cell Tracking Challenge and T98G glioblastoma cell electrotaxis, achieving tracking accuracies of 0.983, 0.999, and 0.999, respectively. Beyond standard tracking accuracy metrics, the Tui tracker demonstrates superior performance on biologically motivated lineage fidelity measures such as BIO (averaging complete track reconstruction, branching correctness, track continuity, and cell cycle accuracy) and OP

Identifiers

PMID42040257
PMCPMC13106940

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.