A device-restricted C. elegans whole-brain recording dataset for ASCENT: annotation-free neuron tracking benchmark
Authors/Creators
Description
This dataset accompanies the manuscript “Annotation-free whole-brain neuron tracking via transformer-based self-supervised learning” by Han et al. The manuscript introduces ASCENT, an Annotation-free Self-supervised Contrastive Embeddings for 3D Neuron Tracking method.
It provides the in-house whole-brain (head ganglion) recording of C. elegans used for benchmarking and analysis in the ASCENT study, along with the corresponding ground-truth and predicted neuron detections and tracks.
The recording captures neuronal activity across the head ganglion of a C. elegans (strain ZM9624, expressing pan-neuronal GCaMP6 and mNeptune) confined within a microfluidic device that permits controlled chemical stimulation. Worms were subjected to repeated cycles of 60s buffer followed by 30s E. coli OP50 supernatant stimulus. Imaging was performed on a Bruker Opterra II swept-field confocal microscope using a 40× 0.75 NA air objective and a Photometrics Evolve 512 Delta EM-CCD camera. The dataset consists of 1,100 volumetric frames (2 × 9 × 512 × 512; channels × z × y × x) acquired at 3.3 Hz with a spatial resolution of 1.5 μm (z) and 0.243 μm (xy).
Ground-truth neuron tracks were generated by manually proofreading and correcting an initial set of tracks generated by ZephIR (https://github.com/venkatachalamlab/ZephIR). These ground-truth and ASCENT-predicted detections and tracks are provided in CSV format compatible with Napari and standard cell-tracking frameworks.
Files included
- InHouse_Opterra_CH_RG_Day1_OP50Stim_1100frames.h5
- Raw volumetric imaging data
- Structure:
root/ ├── t0/c0 (Z × Y × X) ├── t1/c0 ... - c0: mNeptune, c1: GCaMP6
- InHouse_Opterra_coords_GT.csv
- Ground-truth neuron detections.
- Columns: object_id,t,z,y,x
- InHouse_Opterra_coords_PRED.csv
- ASCENT-predicted neuron detections.
- Columns: object_id,t,z,y,x
- InHouse_Opterra_tracks_GT.csv
- Ground-truth neuron tracks (Napari-compatible).
- Columns: TrackID,ObjectID,t,z,y,x
- InHouse_Opterra_tracks_PRED.csv
- ASCENT-predicted neuron tracks (Napari-compatible).
- Columns: TrackID,ObjectID,t,z,y,x
Usage notes
- The dataset can be opened using standard HDF5 libraries (e.g., h5py in Python) or visualized in Napari with the napari-ndtiffs or napari-hdf5 plugins.
- Detection and tracking CSVs can be loaded directly as points and tracks layers in Napari.
- The dataset follows the ASCENT data I/O conventions described in the associated manuscript and GitHub repository: https://github.com/lu-lab/ascent.
- Coordinates are in voxel units consistent with the recorded voxel size.
Files
InHouse_Opterra_coords_GT.csv
Files
(6.3 GB)
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md5:9581892c969bb0e7f3d84eba84988133
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6.2 GB | Download |
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md5:37bd1234033d360dfd888b46daa369b1
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2.1 MB | Preview Download |
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md5:2e90f28f008b7b4c5ff556e9cb065ce0
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9.0 MB | Preview Download |
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md5:5a7c414b95365bb13cc2674df03d5277
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md5:149de39424a2cc1f48507b5e5813c279
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3.1 MB | Preview Download |
Additional details
Dates
- Submitted
-
2025-11-09