This repository contains the released multi-hypothesis instance segmentation masks for the paper "An annotated timelapse imaging dataset on dormancy exit dynamics of Escherichia coli cells in Mother Machine".

The release consists of one Zarr store:

20260307_SB7_exit_snake_V4_1.segmentation_masks_multi_epoch_uint8_masks_only.zarr

The store occupies approximately 4.6 GB on disk and contains only the integer-labelled mask hypotheses used for downstream analysis and tracking. The auxiliary Omnipose distance and flow outputs were omitted from this release to keep the archive practical to distribute.

What is in this repository

The main array is:

data(hypothesis, trench, time, y, x) with shape (17, 2795, 721, 164, 34)

and:

dtype: uint8
chunks: (1, 1, 721, 164, 34)
axis names: ["hypothesis", "trench", "time", "y", "x"]

Each data[h, trench, time] slice is a single instance-labelled segmentation mask for one trench at one timepoint. Background is labelled 0; cell instances are stored as positive integer labels.

The hypothesis axis corresponds to Omnipose inference at different saved training checkpoints:

[50, 300, 550, 800, 1050, 1300, 1550, 1800, 2050, 2300, 2550, 2800, 3050, 3300, 3550, 3800, 3999]

These checkpoint values are stored in:

data.attrs["hypothesis_epoch_values"]

Metadata included in the store

Although this is a masks-only release, the store includes enough metadata to map masks back to the experiment and trench geometry. Root attributes include:

source_nd2
source_zarr
experiment_name
pixel_size_um
n_trenches, n_times, trench_height, trench_width
registration_params
source_metadata_version
source_acquisition_metadata
source_subset_metadata
segmentation_hypothesis_epochs
segmentation_model_checkpoint_template
segmentation_run_stem
segmentation_params

The store also includes a full trench_mapping table in the root attributes, giving for each trench:

trench_id
source fov
lane_index
crop bounds x_left, x_right, y_top, y_bottom
orientation
needs_flip

This means the mask archive can be interpreted on its own for segmentation benchmarking and hypothesis-aware tracking, although users who need the underlying images should pair it with the companion trench image store.

Experimental context

The masks correspond to extracted trench movies of E. coli K-12 MG1655 cells in a mother-machine experiment monitoring dormancy exit. Cells were held under spent LB plus pluronic for the first 60 min and then switched to fresh LB plus pluronic to induce resuscitation. Imaging was performed at approximately 30 s intervals for 721 frames.

The original image data can be found at https://huggingface.co/datasets/ghardo/scientific_data_2026_images

How the masks were generated

Synthetic training data were generated with SyMBac, and an Omnipose model was trained on the resulting mother-machine images. To preserve segmentation uncertainty, inference was run at multiple saved checkpoints rather than exporting only a single final mask set.

The released masks therefore represent 17 alternative segmentation hypotheses for the same underlying trench movies. In downstream use, these hypotheses can be:

compared to quantify segmentation stability across checkpoints
used as input to multi-hypothesis or repair-based tracking workflows
reduced to a single preferred hypothesis if only one segmentation per frame is needed

In the associated tracking workflow, the final-checkpoint masks are used for an initial Trackastra pass, and ambiguous local intervals are then revisited using the full hypothesis set.

Data format and access

This is a directory-style Zarr store and is best accessed with zarr, dask.array, or other Zarr-aware tools.

Minimal `zarr` example

import zarr

root = zarr.open(
    "20260307_SB7_exit_snake_V4_1.segmentation_masks_multi_epoch_uint8_masks_only.zarr",
    mode="r",
)

masks = root["data"]  # (hypothesis, trench, time, y, x)
epochs = masks.attrs["hypothesis_epoch_values"]
pixel_size_um = root.attrs["pixel_size_um"]

`dask.array` example

import dask.array as da

masks = da.from_zarr(
    "20260307_SB7_exit_snake_V4_1.segmentation_masks_multi_epoch_uint8_masks_only.zarr/data"
)

print(masks.shape)   # (17, 2795, 721, 164, 34)
print(masks.dtype)   # uint8

Selecting one hypothesis

import zarr

root = zarr.open(
    "20260307_SB7_exit_snake_V4_1.segmentation_masks_multi_epoch_uint8_masks_only.zarr",
    mode="r",
)

masks = root["data"]
epochs = masks.attrs["hypothesis_epoch_values"]

final_checkpoint_masks = masks[-1]   # epoch 3999
early_checkpoint_masks = masks[0]    # epoch 50

Recommended uses

This dataset is well suited to:

benchmarking bacterial instance segmentation under low-contrast mother-machine imaging
testing multi-hypothesis tracking methods
measuring checkpoint-to-checkpoint segmentation agreement
extracting morphology from alternative mask hypotheses
studying how segmentation uncertainty propagates into downstream lineage reconstruction

Limitations

This repository contains masks only, not the trench image data.
The release omits Omnipose distance and flow fields.
The masks represent agreement across checkpoints of one training pipeline, not independent human ground truth.
Labels are stored as uint8, so this format assumes the number of instances per trench frame fits within that range.
The store is not packaged for the Hugging Face datasets tabular loader; use Zarr-aware tools directly.

Companion data

For the underlying image data and full acquisition metadata, use the companion image repository (https://huggingface.co/datasets/ghardo/scientific_data_2026_images) containing:

20260307_SB7_exit_snake_V4_1_with_metadata.trenches.zarr

Together, the image store and the mask-hypothesis store support segmentation benchmarking, morphology extraction, and lineage reconstruction workflows.

Repository structure

20260307_SB7_exit_snake_V4_1.segmentation_masks_multi_epoch_uint8_masks_only.zarr/
├── zarr.json
└── data/
    └── zarr.json and chunk data

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Mask data for Hardo, Li, and Bakshi, 2026

Dataset summary