DnaRnaProteins/ctc-cell-cycle-hela
CTC Cell Cycle Dataset Cell Tracking Challenge (CTC) live-cell microscopy with derived cell cycle state labels for 3-class temporal classification. What's actually hosted The repo name says hela for historical reasons. Currently hosted: Fluo-N2DH-GOWT1 (GFP-tagged Oct4 in mouse embryonic stem cells), which is what the milestone baseline trained on. HeLa data may be added later under a hela/ prefix. Sequence Frames Used as 01/ 92 training 02/ 92… See the full description on the dataset page: https://huggingface.co/datasets/DnaRnaProteins/ctc-cell-cycle-hela.
CTC Cell Cycle Dataset
Cell Tracking Challenge (CTC) live-cell microscopy with derived cell cycle state labels for 3-class temporal classification.
What's actually hosted
The repo name says hela for historical reasons. Currently hosted: Fluo-N2DH-GOWT1 (GFP-tagged Oct4 in mouse embryonic stem cells), which is what the milestone baseline trained on. HeLa data may be added later under a hela/ prefix.
Each sequence ships with the CTC ground-truth tracking under <seq>_GT/TRA/ (man_track*.tif masks + man_track.txt lineage) and segmentation under <seq>_GT/SEG/.
Label schema
Labels are derived algorithmically from lineage tree bifurcations — not annotated by hand.
Class distribution (seq 01): interphase ~98%, pre-mitosis ~1.2%, mitosis ~0.6%. Use class weights or balanced sampling.
Examples
samples/ contains rendered PNG previews:
frame_full_*.png— full-frame examples at different timepointscrop_interphase.png,crop_premitosis.png,crop_mitosis.png— class examples
Loading
from huggingface_hub import snapshot_download
root = snapshot_download(repo_id="DnaRnaProteins/ctc-cell-cycle-hela", repo_type="dataset")
# Use with project/src/cell_cycle/datasets/ctc.py:CTCClipDatasetSource
MICCAI Cell Tracking Challenge Fluo-N2DH-GOWT1. Bartosova et al., FEBS Letters 2011 (original imaging); Maska et al., Nature Methods 2023; Ulman et al., Nature Methods 2017.
