CellImageNet

⚠️ Release v0.1.0 (partial). This is an initial partial release. The full corpus is 42 Xenium samples (28 human + 14 mouse); additional tissues are being uploaded as they finish processing. The figures below describe the complete CellImageNet — the set currently available on the Hub is a subset and is growing toward the full 42. See Release status for exactly what is uploaded right now.

CellImageNet is a large-scale single-cell image database of paired DAPI nuclear images with cell-type annotations, built from publicly available 10x Genomics Xenium data. It contains ~10 million cells from 42 Xenium samples — 28 human and 14 mouse tissues — spanning diverse species, biological conditions, and tissue types, annotated with 31 harmonized cell-type classes (unified from the source datasets' own annotations into a common label set).

Each cell has paired DAPI crops centered on the same cell at complementary context scales:

• 2.5× — a tight view capturing fine nuclear morphology, and
• 10× — a wider view capturing the local tissue context / neighbourhood.

Crops are provided at their native resolution (variable per sample; they are not pre-resized — resize to a fixed input size, e.g. 224×224, is left to the downstream model).

Configurations & splits

config	content
human	28 human Xenium samples (~6.5M cells)
mouse	14 mouse Xenium samples (~3.4M cells)

(Counts are pre-filtering segmentation totals; the released set is marginally smaller after removing cells with tiny nuclear masks or missing crops.)

This is an unsplit corpus: each config exposes a single full split (we do not ship an official train/test partition). The exact subset used to train MorphPT is specified in the MorphPT weights repo under splits/.

from datasets import load_dataset
ds = load_dataset("jilab/CellImageNet", "human", split="full", streaming=True)
ex = next(iter(ds))
ex["2p5x.png"], ex["10x.png"], ex["json"]["cell_type"]

Release status

v0.1.0 (partial) — samples are being added as they finish processing (target: 28 human + 14 mouse = 42).

Currently available: 9 human, 0 mouse samples.

• Xenium_Preview_Human_Lung_Cancer
• Xenium_Preview_Human_Non_diseased_Lung
• Xenium_Prime_Ovarian_Cancer_FFPE
• Xenium_V1_FFPE_Human_Brain_Alzheimers
• Xenium_V1_FFPE_Human_Brain_Glioblastoma
• Xenium_V1_FFPE_Human_Brain_Healthy
• Xenium_V1_hColon_Cancer_Add_on
• Xenium_V1_hColon_Cancer_Base
• Xenium_V1_hColon_Non_diseased_Add_on

The authoritative, always-current list of source samples (with 10x URLs and per-sample cell counts) is in attribution_manifest.csv.

Sample schema (WebDataset)

Each sample (one cell) is keyed by cell_id with three members:

member	type	description
2p5x.png	image	2.5× DAPI crop (grayscale, native resolution)
10x.png	image	10× DAPI crop (grayscale, native resolution)
json	dict	metadata (below)

json fields: cell_id, dataset (source Xenium sample), species (Human/Mouse), tissue, condition, cell_type (one of the 31 classes below, plus a small Unknown bucket in some mouse samples), x_centroid, y_centroid (nuclear centroid, microns).

Note: the field is named cell_type (the fine-grained cell label). It is not the coarse morphology "group" used by the MorphPT router — that grouping lives in the model repo, not in this dataset.

Cell-type classes

The 31 harmonized cell-type labels in cell_type:

All 31 classes

Astrocytes · B cells · Brain cancer cells · Cardiac muscle cells · Chondrocytes · Colon cancer cells · Endothelial cells · Ependymal cells · Epithelial cells · Erythrocytes · Fibroblasts · Kidney cancer cells · Liver cancer cells · Lung cancer cells · Mesangial cells · Microglia · Myeloid cells · NK cells · Neurons · OPCs · Oligodendrocytes · Ovary cancer cells · Pancreas cancer cells · Pericytes · Schwann cells · Skeletal muscle cells · Skin cancer cells · Smooth muscle cells · Stem and progenitor cells · Stromal cells · T cells

Unknown is mouse-only (~134k cells, ≈3.8% of the mouse split; no human cell carries it) and marks cells left unannotated in the source. Filter it out if you need a clean 31-class label space.

How it was built

Source: 42 Xenium samples (28 human, 14 mouse) from the 10x Genomics datasets portal. From each tissue-wide DAPI image we used the morphology_mip maximum-intensity-projection channel (or morphology_focus when unavailable). Nuclear segmentation masks (10x Xenium Onboard Analysis) were converted to pixels at 0.2125 µm/px; cells with rasterized nuclear area < 5 px or a bounding box < 10 px in either dimension were removed. For each cell, two square crops centred on the nuclear centroid were extracted at context scales r = 2.5 and r = 10 (side length S_r = r·d, with d the per-sample mean nuclear bounding-box size) and zero-padded at image borders. Crops are stored at native resolution.

License & attribution

CellImageNet is a derivative work of publicly available 10x Genomics Xenium datasets. The underlying imaging data is distributed by 10x Genomics under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Because CellImageNet is derived from CC BY 4.0 material, the image crops are released under CC BY 4.0; the cell-type annotations and derived metadata contributed by the CellImageNet authors are likewise released under CC BY 4.0. See LICENSE for the full statement.

Under CC BY 4.0 you may share and adapt this dataset, including commercially, provided you (1) credit 10x Genomics and the CellImageNet authors, (2) link the license, and (3) indicate that changes were made — the images here have been cropped/re-framed and re-annotated and are not the original 10x Genomics files.

Source datasets

All 42 source samples are 10x Genomics Xenium In Situ datasets from the 10x Genomics datasets portal. Each is individually licensed CC BY 4.0 on its dataset page. The complete list of source samples (dataset name, species, tissue, condition, and its 10x dataset URL) is provided in attribution_manifest.csv in this repository.

Please cite both 10x Genomics and the individual source datasets in addition to the CellImageNet/MorphPT paper below.

Limitations

• DAPI only — nuclear morphology, no gene expression or protein channels (despite deriving from Xenium spatial-transcriptomics runs).
• Native-resolution crops vary in pixel size across samples; downstream models must resize to a fixed input.
• Unsplit and imbalanced — no official train/test split, and class frequency is highly skewed (tissue/condition sampling reflects the source datasets, not a balanced design). Subsample or reweight for classifier training.
• Labels are the source annotations harmonized into 31 classes; ≈3.8% of mouse cells (none in human) are Unknown.

Relation to MorphPT

CellImageNet is the training corpus for MorphPT, a visual foundation model for cell classification. MorphPT was trained on a human-only, per-class subsampled subset of CellImageNet.

• Code: https://github.com/AnitaCao/MorphPT
• Model weights: https://huggingface.co/jilab/MorphPT

Citation

@article{cao2026visual,
  title   = {A visual foundation model for cell classification},
  author  = {Cao, Ting and Zhuang, Haotian and Zhang, Boxuan and
             Pang, Zhiping P. and Tang, Ruixiang and Liu, Dongfang and
             Ji, Zhicheng},
  year    = {2026}
}