Published July 6, 2026 | Version v1
Dataset Open

Datasets from "A general method for bootstrapping dense 3D segmentations from sparse 2D annotations"

Authors/Creators

  • 1. EDMO icon The University of Texas at Austin
  • 2. E11 Bio
  • 3. ROR icon University of California San Diego

Description

Image volumes used by the bootstrapper_nm_capsule (https://github.com/ucsdmanorlab/bootstrapper_nm_capsule), a reproduction of "A general method for bootstrapping dense 3D segmentations from sparse 2D annotations." The method turns a sparse 2D annotation budget into a dense 3D instance segmentation (pseudo ground-truth), then shows that a 3D network bootstrapped from that pseudo-GT approaches one trained on dense ground truth.

This record is a single archive, data.tar (~3.9 GB). It unpacks to data/<dataset>/, one directory per dataset, each holding zarr (https://zarr.dev) arrays:

- Core datasets (cremi_a, cremi_b, cremi_c, epi, fib, harris15) have volume_1.zarr/{raw,labels,sparse_labels} and a held-out volume_2.zarr/{raw,labels}.
- Single-volume datasets (liconn, mitoem, cremi_clefts, fluo, prism) have only volume_1.zarr, with an added sparse_labels_mask.

Modalities and voxel sizes (z, y, x in nm): cremi_a/b/c — EM Drosophila neuropil (40, 8, 8); epi — plant epithelium (235, 75, 75); fib — FIB-SEM, isotropic (8, 8, 8); harris15 — EM hippocampal neuropil (50, 8, 8); liconn — expansion-microscopy LM (24, 18, 18); mitoem — EM mitochondria (30, 8, 8); cremi_clefts — EM synaptic clefts (40, 4, 4); fluo — fluorescence, 2D+time (1, 1, 1); prism — PRISM expansion LM, 18-channel (400, 168, 168).

Usage: download data.tar and run tar -xf data.tar (or use ./download_data.sh in the repo, which fetches this record and unpacks it into data/). Code, environment, and full run instructions are in the GitHub repository above.

These are publicly available datasets, consolidated and reformatted to zarr for this reproduction. Please cite the original dataset sources as well as this archive.

Files

Files (3.9 GB)

Name Size
md5:056f27aec65c5b8d5a7f831b99619897
3.9 GB Download

Additional details

Related works

Is referenced by
Preprint: https://www.biorxiv.org/content/10.1101/2024.06.14.599135v3 (URL)

Software

Repository URL
https://github.com/å/bootstrapper_nm_capsule
Development Status
Active

References

  • CREMI: MICCAI Challenge on Circuit Reconstruction from Electron Microscopy Images. https://cremi.org/
  • Takemura, S. et al. "Synaptic circuits and their variations within different columns in the visual system of Drosophila." PNAS 112, 13711–13716 (2015)
  • Harris, K. M. et al. "A resource from 3D electron microscopy of hippocampal neuropil for user training and tool development." Sci. Data 2, 150046 (2015)
  • Wolny, A. et al. "Accurate and versatile 3D segmentation of plant tissues at cellular resolution." eLife 9, e57613 (2020)
  • Wei, D. et al. "MitoEM Dataset: Large-Scale 3D Mitochondria Instance Segmentation from EM Images." MICCAI 2020, LNCS 12265, 66–76. https://doi.org/10.1007/978-3-030-59722-1_7
  • Ulman, V. et al. "An objective comparison of cell-tracking algorithms." Nat. Methods 14, 1141–1152 (2017). https://doi.org/10.1038/nmeth.4473
  • Tavakoli, M. R., Lyudchik, J., … Danzl, J. G. "Light-microscopy-based connectomic reconstruction of mammalian brain tissue." Nature (2025). https://doi.org/10.1038/s41586-025-08985-1
  • Park, S. Y. et al. "Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context." bioRxiv 2025.09.26.678648 (2025). https://doi.org/10.1101/2025.09.26.678648