| --- |
| license: gpl-3.0 |
| task_categories: |
| - image-feature-extraction |
| - image-classification |
| - image-segmentation |
| language: |
| - en |
| tags: |
| - biology |
| - cells |
| - microbiology |
| size_categories: |
| - 10M<n<100M |
| --- |
| |
| --- |
|
|
| --- |
|
|
| license: gpl-3.0 |
| pretty_name: HASSL SingleCellBench |
| task_categories: |
|
|
| * image-segmentation |
| * image-classification |
| tags: |
| * single-cell |
| * nuclei-segmentation |
| * cell-segmentation |
| * instance-segmentation |
| * biomedical-imaging |
| * microscopy |
| * histology |
| * fluorescence-microscopy |
| * benchmark |
| * computer-vision |
| size_categories: |
| * 1M<n<10M |
| |
| --- |
| |
| # HASSL SingleCellBench |
| |
| **HASSL SingleCellBench** is a curated benchmark collection for single-cell, nuclei, and biomedical instance segmentation research. It aggregates selected public microscopy datasets into a unified folder structure for training, evaluation, and benchmarking across heterogeneous imaging modalities, cell types, tissue contexts, and annotation schemes. |
| |
| This release contains the subset of datasets that could be redistributed in the uploaded benchmark package. Some datasets referenced in the original HASSL dataset overview were intentionally omitted because of licensing or redistribution constraints. |
| |
| ## License |
| |
| This repository is released under the **GNU General Public License v3.0 (GPL-3.0)**. |
| |
| Important note: this repository aggregates processed versions of multiple third-party benchmark datasets. The GPL-3.0 license applies to the repository packaging, preprocessing code, loaders, and provided metadata where applicable. The underlying datasets may remain subject to their original licenses, citation requirements, and redistribution terms. Users are responsible for complying with the terms of each original dataset. |
| |
| ## Data Format |
| |
| The dataset is stored as NumPy arrays. |
| |
| For each sample directory: |
| |
| * `original/` contains `.npy` files with the extracted single-cell or nucleus image. |
| * `mask/` contains `.npy` files with the corresponding segmentation mask. |
| |
| A typical segmentation sample path looks like: |
| |
| ```text |
| N_BCCD/train/original/example.npy |
| N_BCCD/train/mask/example.npy |
| ``` |
| |
| For datasets with class-level folders, the same pattern is nested under the relevant class: |
| |
| ```text |
| N_CoNIC/train/epithelial/original/example.npy |
| N_CoNIC/train/epithelial/mask/example.npy |
| ``` |
| |
| |
| ## Included Datasets |
| |
| The following datasets are included in this uploaded release: |
| |
| | Folder | Dataset | Cells | Labels | Modality | Cell / Object Types | Original Ref. | |
| | --------------------- | ---------------------------------------------: | ------: | --------- | ------------------------------- | ----------------------------------------------------------- | ------------- | |
| | `N_BCCD` | BCCD | 90,813 | U | Brightfield blood smear | Blood cells | [13] | |
| | `N_CoNIC` | CoNIC | 7,696 | Annotated | H&E histology | Colon epithelial, stromal, immune, neutrophils, eosinophils | [20] | |
| | `N_cyto2` | Cyto / Cyto2 | 71,783 | U | Mixed microscopy | Mixed cultured cells | [44, 51] | |
| | `N_databowl` | Data Science Bowl 2018 | 14,902 | U | Mixed IF / BF | Mixed species nuclei | [19] | |
| | `N_DynamicNuclearNet` | Dynamic Nuclear Net | 347,572 | U | Live-cell fluorescence | Cultured human nuclei | [54] | |
| | `N_iPSC_Morpologies` | iPSC morphology data | 35,308 | Annotated | Multichannel microscopy | Human induced pluripotent stem cells | [45] | |
| | `N_iPSC_QCData` | iPSC quality-control data | 35,308 | Annotated | Multichannel microscopy | Human induced pluripotent stem cells | [45] | |
| | `N_lynsec13` | LynSec | 70,676 | Annotated | H&E histology | Lymphoma cells, DLBCL | [29] | |
| | `N_MoNuSAC` | MoNuSAC | 28,744 | Annotated | H&E histology | Neoplastic, lymphocyte, macrophage, neutrophil | [55] | |
| | `N_MoNuSeg` | MoNuSeg | 16,031 | U | H&E histology | Mixed tumor and stromal nuclei | [35] | |
| | `N_NuInsSeg` | NuInsSeg | 25,293 | Annotated | H&E histology | 31-organ nuclei | [39] | |
| | `N_omnipose` | Omnipose | 37,038 | Annotated | Phase contrast and fluorescence | Bacterial and other cells | [11] | |
| | `N_PanNuke` | PanNuke | 104,594 | Annotated | H&E histology | Tumor, immune, epithelial, stromal, dead | [17, 18] | |
| | `N_Satorious` | Sartorius Cell Instance Segmentation Challenge | 34,621 | Annotated | Phase contrast | Cortical neurons, astrocytes, SH-SY5Y | [27] | |
| | `N_tissuenet` | TissueNet | 866,884 | U | Multiplex immunofluorescence | Multiple tissue cell types | [53] | |
| |
| `U` indicates that the original dataset does not provide explicit annotated cell-type labels in the HASSL benchmark table. For those datasets, the dataset name or folder-level class is used as the label. |
| |
| ## Datasets Referenced in the HASSL Overview but Not Included Here |
| |
| The original HASSL dataset overview contains additional datasets that are **not present in this uploaded release**. These were omitted because of licensing, redistribution, or packaging constraints. |
| |
| Not included in this release: |
| |
| * CPM 15+17 and TNBC |
| * CryoNuSeg |
| * IHC TMA |
| * NeurIPS 2022 Cell-Seg |
| * Phenoplex |
| * YeaZ |
| |
| Users who need these datasets should obtain them directly from the original sources and comply with their individual licenses and access requirements. |
| |
| ## Repository Structure |
| |
| The uploaded dataset follows a hierarchical `dataset / split / class / original + mask` layout. Depending on the source dataset, class folders may appear before or after the train/test split. |
| |
| ```text |
| . |
| ├── Dataloader_example.ipynb |
| ├── N_BCCD |
| │ ├── test |
| │ │ ├── mask |
| │ │ └── original |
| │ └── train |
| │ ├── mask |
| │ └── original |
| ├── N_CoNIC |
| │ ├── test |
| │ │ ├── connective |
| │ │ │ ├── mask |
| │ │ │ └── original |
| │ │ ├── epithelial |
| │ │ │ ├── mask |
| │ │ │ └── original |
| │ │ ├── esoinophil |
| │ │ │ ├── mask |
| │ │ │ └── original |
| │ │ ├── lymphocyte |
| │ │ │ ├── mask |
| │ │ │ └── original |
| │ │ ├── neutrophil |
| │ │ │ ├── mask |
| │ │ │ └── original |
| │ │ └── plasma |
| │ │ ├── mask |
| │ │ └── original |
| │ └── train |
| │ ├── connective |
| │ ├── epithelial |
| │ ├── esoinophil |
| │ ├── lymphocyte |
| │ ├── neutrophil |
| │ └── plasma |
| ├── N_DynamicNuclearNet |
| │ ├── test |
| │ │ ├── mask |
| │ │ └── original |
| │ └── train |
| │ ├── mask |
| │ └── original |
| ├── N_MoNuSAC |
| │ ├── Epithelial |
| │ │ ├── test |
| │ │ │ ├── mask |
| │ │ │ └── original |
| │ │ └── train |
| │ │ ├── mask |
| │ │ └── original |
| │ ├── Lymphocyte |
| │ ├── Macrophage |
| │ └── Neutrophil |
| ├── N_MoNuSeg |
| │ ├── test |
| │ │ ├── mask |
| │ │ └── original |
| │ └── train |
| │ ├── mask |
| │ └── original |
| ├── N_NuInsSeg |
| │ ├── human bladder |
| │ ├── human brain |
| │ ├── human cardia |
| │ ├── human cerebellum |
| │ ├── human epiglottis |
| │ ├── human jejunum |
| │ ├── human kidney |
| │ ├── human liver |
| │ ├── human lung |
| │ ├── human melanoma |
| │ ├── human muscle |
| │ ├── human oesophagus |
| │ ├── human pancreas |
| │ ├── human peritoneum |
| │ ├── human placenta |
| │ ├── human pylorus |
| │ ├── human rectum |
| │ ├── human salivory gland |
| │ ├── human spleen |
| │ ├── human testis |
| │ ├── human tongue |
| │ ├── human tonsile |
| │ ├── human umbilical cord |
| │ ├── mouse fat (white and brown)_subscapula |
| │ ├── mouse femur |
| │ ├── mouse heart |
| │ ├── mouse kidney |
| │ ├── mouse liver |
| │ ├── mouse muscle_tibia |
| │ ├── mouse spleen |
| │ └── mouse thymus |
| ├── N_PanNuke |
| │ ├── Adrenal_gland |
| │ │ ├── Connective |
| │ │ │ ├── test |
| │ │ │ └── train |
| │ │ ├── Dead |
| │ │ ├── Epithelial |
| │ │ ├── Inflammatory |
| │ │ └── Neoplastic |
| │ ├── Bile-duct |
| │ ├── Bladder |
| │ ├── Breast |
| │ ├── Cervix |
| │ ├── Colon |
| │ ├── Esophagus |
| │ ├── HeadNeck |
| │ ├── Kidney |
| │ ├── Liver |
| │ ├── Lung |
| │ ├── Ovarian |
| │ ├── Pancreatic |
| │ ├── Prostate |
| │ ├── Skin |
| │ ├── Stomach |
| │ ├── Testis |
| │ ├── Thyroid |
| │ └── Uterus |
| ├── N_Satorious |
| │ ├── test |
| │ │ ├── astrocytes |
| │ │ │ ├── mask |
| │ │ │ └── original |
| │ │ ├── neurons |
| │ │ │ ├── mask |
| │ │ │ └── original |
| │ │ └── shsy5y |
| │ │ ├── mask |
| │ │ └── original |
| │ └── train |
| │ ├── astrocytes |
| │ ├── neurons |
| │ └── shsy5y |
| ├── N_cyto2 |
| │ ├── test |
| │ │ ├── mask |
| │ │ └── original |
| │ └── train |
| │ ├── mask |
| │ └── original |
| ├── N_databowl |
| │ ├── test |
| │ │ ├── mask |
| │ │ └── original |
| │ └── train |
| │ ├── mask |
| │ └── original |
| ├── N_iPSC_Morpologies |
| │ ├── Big |
| │ ├── Long |
| │ ├── Mitotic |
| │ ├── RAR-treated |
| │ └── Round |
| ├── N_iPSC_QCData |
| │ ├── Cell |
| │ ├── Debris |
| │ ├── DyingCell |
| │ └── MitoticCell |
| ├── N_lynsec13 |
| │ ├── negative |
| │ ├── non-tumor |
| │ ├── positive |
| │ └── tumor |
| ├── N_omnipose |
| │ ├── bact_fluor_A22 |
| │ ├── bact_fluor_bthai |
| │ ├── bact_fluor_cex |
| │ ├── bact_fluor_vibrio |
| │ ├── bact_fluor_wiggins |
| │ ├── bact_phase_5I_crop |
| │ ├── bact_phase_A22 |
| │ ├── bact_phase_PAO1_staph |
| │ ├── bact_phase_PSVB |
| │ ├── bact_phase_bthai |
| │ ├── bact_phase_caulo |
| │ ├── bact_phase_cex |
| │ ├── bact_phase_dnaA |
| │ ├── bact_phase_ecoli_mut |
| │ ├── bact_phase_francisella |
| │ ├── bact_phase_ftsN |
| │ ├── bact_phase_hpylori |
| │ ├── bact_phase_murA |
| │ ├── bact_phase_serratia |
| │ ├── bact_phase_streptomyces |
| │ ├── bact_phase_vibrio |
| │ └── bact_phase_wiggins |
| └── N_tissuenet |
| ├── test |
| │ ├── mask |
| │ └── original |
| └── train |
| ├── mask |
| └── original |
| ``` |
| |
| For compactness, repeated subtrees are abbreviated in the tree above. In general, all terminal train/test sample folders follow one of these patterns: |
| |
| ```text |
| .../train/original/*.npy |
| .../train/mask/*.npy |
| .../test/original/*.npy |
| .../test/mask/*.npy |
| ``` |
| |
| ## Download |
| |
| Using the Hugging Face Hub Python library: |
| |
| ```python |
| from huggingface_hub import snapshot_download |
| |
| dataset_path = snapshot_download( |
| repo_id="tum-ai/HASSL-SingleCellBench", |
| repo_type="dataset" |
| ) |
| |
| print(dataset_path) |
| ``` |
| |
| Using the Hugging Face CLI: |
| |
| ```bash |
| hf download tum-ai/HASSL-SingleCellBench --repo-type=dataset |
| ``` |
| |
| ## If Downloaded as Zip Archives |
| |
| If this repository is distributed as one `.zip` archive per top-level dataset folder, extract all archives with: |
| |
| ```bash |
| mkdir -p extracted |
| |
| for f in *.zip; do |
| unzip -q "$f" -d extracted/ |
| done |
| ``` |
| |
| ## Example Usage |
| |
| A basic dataloader example is provided in: |
| |
| ```text |
| Dataloader_example.ipynb |
| ``` |
| |
| This notebook demonstrates how the processed benchmark folders can be loaded for downstream model training and evaluation. |
| |
| ## Intended Use |
| |
| This dataset is intended for: |
| |
| * single-cell and nuclei segmentation benchmarking |
| * biomedical instance segmentation |
| * cross-dataset generalization experiments |
| * microscopy representation learning |
| * robust evaluation across heterogeneous cell and tissue domains |
| * development of unified dataloaders for biomedical segmentation datasets |
| |
| ## Limitations |
| |
| The dataset combines benchmarks with different image modalities, preprocessing conventions, annotation schemes, label definitions, and split structures. Users should inspect each folder before training or evaluation and verify that the image-mask pairing, labels, and splits match their experimental setup. |
| |
| The included release is not identical to the full dataset list described in the original HASSL overview. Some referenced datasets were excluded from this upload because of licensing or redistribution restrictions. |
| |
| ## Citation |
| |
| If you use this benchmark package, please cite this repository and the original datasets used in your experiments. |
| |
| ```bibtex |
| @dataset{hassl_singlecellbench, |
| title = {HASSL SingleCellBench}, |
| author = {TUM.ai}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/datasets/tum-ai/HASSL-SingleCellBench}, |
| license = {GPL-3.0} |
| } |
| ``` |
| |
| Original dataset references from the HASSL benchmark table: |
| |
| ```text |
| [11] Omnipose: Cutler et al., Nature Methods, 2022. |
| [13] BCCD: Depto et al., Tissue and Cell, 2021. |
| [17] PanNuke: Gamper et al., European Congress on Digital Pathology, 2019. |
| [18] PanNuke extension: Gamper et al., arXiv, 2020. |
| [19] Data Science Bowl 2018, Kaggle, 2018. |
| [20] CoNIC: Graham et al., arXiv, 2021. |
| [27] Sartorius Cell Instance Segmentation Challenge, Kaggle, 2021. |
| [29] LynSec: Hussein et al., Zenodo, 2023. |
| [35] MoNuSeg: Kumar et al., IEEE Transactions on Medical Imaging, 2020. |
| [39] NuInsSeg: Mahbod et al., arXiv, 2023. |
| [44] Cellpose-SAM: Pachitariu et al., bioRxiv, 2025. |
| [45] iPSC morphology dataset: Pfaendler, ETH Zurich Research Collection, 2022. |
| [51] Cellpose: Stringer et al., bioRxiv, 2020. |
| [53] TissueNet: Van Valen Lab, DeepCell datasets, 2022. |
| [54] Dynamic Nuclear Net: Van Valen Lab, DeepCell datasets, 2023. |
| [55] MoNuSAC: Verma et al., IEEE Transactions on Medical Imaging, 2021. |
| ``` |
| |
| ## Maintainers |
| |
| This dataset is maintained by TUM.ai and Marr Lab @ Helmholtz Munich as part of the HASSL single-cell benchmarking workflow. |
