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Dataset . 2022
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Dataset . 2022
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Dataset . 2022
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Test datasets for segmentation (2D, 3D, timelapses)

Abstract

Datasets for testing: 1) Arabidopsis in 3D with membrane labelling for testing Unet, Stardist and denoising models. 2) Ascadian embryo in 3D imaged via light sheet for testing Unet and Stardist models. 3) Carcinoma cells 3D + time data imaged under low SNR conditions for testing Unet, Stardist and Denoising models. 4) Tissue in 2D + time for testing Unet models. Data imaged by Mari Tolonen at the University of Copenhagen. 5) Nuclei in 3D. Raw, GT and model prediction. Data imaged by Mari Tolonen at the University of Copenhagen. 6) Nuclei cell mask 2D. Raw, GT and model prediction. Data imaged by Mari Tolonen at the University of Copenhagen

Supported by Grant#: 2021-240715(5022) from Chan Zuckerberg Innitiative and Silicon Valley community foundation.

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selected citations
These citations are derived from selected sources.
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!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
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18