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Cell-ACDC: segmentation, tracking, annotation and quantification of microscopy imaging data (dataset)

Research datakeyboard_double_arrow_right Dataset 04 Jul 2022Publisher:Zenodo
Authors: Padovani, Francesco; Mairhörmann, Benedikt; Lengefeld, Jette; Falter-Braun, Pascal; Schmoller, Kurt;

doi: 10.5281/zenodo.6795124 , 10.5281/zenodo.6795123

Cell-ACDC: segmentation, tracking, annotation and quantification of microscopy imaging data (dataset)

Abstract

This work was supported by the DFG through project 416098229, by the Human Frontier Science Program (career development award to KMS), by the Helmholtz Association, and by the Free State of Bavaria's AI for Therapy (AI4T) Initiative through the Institute of AI for Drug Discovery (AID).

This repository includes all the data generated or analysed during the preparation of Cell-ACDC publication, including test datasets for testing the software. Cell-ACDC is open-source software available on GitHub here.

Related Organizations
Keywords

cell cycle analysis, deep-learning cell segmentation, cell tracking, bioimage analysis, live-cell imaging

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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