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LynnMass/tilebac-ULDM-tiles

539 downloads in 30 days0 likesLast modified 10 Jun 2026View on Hugging Face

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The declared licence (mit) is classified as permissive.

Declared licence: mit, from the tagEvidence: README.md at 59affd0
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Tasks

image-segmentation

Citation text from the card

@article {Massenburg2026.06.08.731030,
	author = {Massenburg, Lynnicia N. and Madugula, Sita S. and Brown, Spenser R. and Bible, Amber N. and Harris, Chanda R. and Retterer, Scott T. and Morrell-Falvey, Jennifer L. and Vasudevan, Rama K. and Williams, Alexis N.},
	title = {TileBac: A Benchmark CryoEM Dataset of Bacteria in Ultralow-Dose Montage Tiles},
	elocation-id = {2026.06.08.731030},
	year = {2026},
	doi = {10.64898/2026.06.08.731030},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Current segmentation models are capable of routine identification of biological features in noisy cryogenic electron microscopy (cryoEM) images. However, there are still challenges with complete segmentation of high boundary, thin objects such as bacterial cell envelopes and flagella. Moreover, ultralow-dose cryoEM images pose as an additional challenge to boundary distinctions between the object and background. Here, we present TileBac, a benchmark dataset of ultralow-dose montage tiles of Pantoea sp. YR343 to segment bacterial inner and outer membranes for evaluation of model effectiveness. We show that foundation models outperform convolutional neural networks at continuous bacterial cell envelope segmentation despite having lower performance metrics. We release the TileBac benchmark dataset on Hugging Face for further insights into model architecture development.Competing Interest StatementThe authors have declared no competing interest.U.S. Department of Energy, Office of Scie

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dataset_licence({ "id": "LynnMass/tilebac-ULDM-tiles" })
signal: "permissive"
stated_licence: "mit"
sources: 0