Myelinated nerve fibre segmentation from the AxonPath datasets: 47 electron microscopy (TEM/STEM) and brightfield images of mouse and rat CNS and PNS tissue in 7 sub-datasets, with 9,899 annotated fibres. Hosted copy: 77 files (27 large images split into strips) and 25,765 polygon annotations (Fibre, InnerCylinder, Axon, Nerve, ROI) imported from the authors' QuPath GeoJSON. License: CC BY 4.0 for all 7 sub-datasets (record, README and SOURCES.csv; the 8 AimSeg corpus callosum images are CC BY 4.0 too). Source: https://zenodo.org/records/23084690. Please cite: Carrillo-Barberà, P., Poveda Sabuco, A., Sgattoni, C., Gomez-Sanchez, J. A., Rondelli, A. M., & Williams, A. (2026). AxonPath datasets: annotated electron microscopy and brightfield images of myelinated nerve fibres (v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.23084690

FluoEcoli-Instance (test split): high-content fluorescence microscopy of Escherichia coli for instance segmentation. This project holds the test split of the 24,702-image FluoEcoli-Instance set: 4,941 image patches (256x256 px, single-channel 16-bit grayscale TIFF) with 39,472 expert-annotated cell instances as polygon annotations, one class (cell). The train (14,820 images) and val (4,941 images) splits can follow. License: CC BY 4.0. Source: https://zenodo.org/records/23389427 (DOI 10.5281/zenodo.23389427). Please cite: Huynh, S. T. et al. FluoEcoli-Instance: a high-content fluorescence microscopy dataset and CBAM-enhanced hybrid task cascade for E. coli instance segmentation. IEEE Journal of Selected Topics in Signal Processing 19(8), 1967-1982 (2025). DOI: 10.1109/JSTSP.2025.3648286.

Nuclei instance segmentation dataset for H&E-stained histology (NuInsSeg, Mahbod et al. 2024, Medical University of Vienna). 665 image patches of 512x512 pixels at 40x magnification from 31 human and mouse organs, with 30,698 manually outlined nuclei as polygon annotations converted from the official labeled masks, plus the dataset's ambiguous-area masks as a separate class. License: CC BY 4.0. Source: https://zenodo.org/records/10518968. Please cite: Mahbod, A. et al. NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images. Scientific Data 11, 295 (2024).

Sperm Chromatin Dispersion (SCD) assay bright-field microscopy images with polygon instance segmentation of sperm cells, classified as Fragmented or Non-fragmented (DNA fragmentation; original Spanish labels "Fragmentado" and "No fragmentado"/"No Fragmentado"). PARTIAL COPY: the published Zenodo archive contains only 1,102 of the record's 2,000 images; slides L01 to L17 and L32 (plus 36 images of L18 and 2 of L19), 898 train images in all, are missing from it. This copy holds 1,098 images (1,920x1,080 PNG; train 710, val 200, test 188, after removing 4 byte-identical duplicates) with 2,014 polygons (701 Fragmented, 1,313 Non-fragmented), and will be completed if the authors re-upload the missing images. License: CC BY 4.0. Source: https://zenodo.org/records/21628868. Please cite: Ornelas Nande, E. J., Adel Domínguez, M. A., Mora Topete, A., Magdaleno Márquez, L. E., Aranguren, I., Valdivia-G, A., & Sanchez, O. D. (2026). Sperm chromatin dispersion microscopy image dataset for instance segmentation [Data set]. Zenodo. https://doi.org/10.5281/zenodo.21628868

Person keypoint subset of COCO 2017 val with openly licensed images only: 605 images whose Flickr license is CC BY 2.0 (413), CC BY-SA 2.0 (189) or No known copyright restrictions (3), with 1,609 people annotated with the 17 COCO keypoints (nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles), COCO visibility flags and bounding boxes. Every image is credited with its Flickr URL and license in its file metadata and in annotations/credits.csv. License: annotations CC BY 4.0 (COCO Consortium); each image keeps its Flickr owner's license. Source: https://cocodataset.org Please cite: Lin, T.-Y. et al., Microsoft COCO: Common Objects in Context. ECCV 2014.

Tomato subset of the CCMT crop pest and disease dataset (Mensah et al., 2023): field photographs of tomato leaves from farms in Ghana, labelled per image with one of 5 classes: healthy, leaf blight, leaf curl, septoria leaf spot and verticulium wilt (the source's spelling of verticillium wilt). 5,325 raw 400x400 JPEG images out of 5,805 in the source's Raw Data/Tomato folders; 25 corrupt JPEGs that do not decode and 454 byte-identical duplicate copies were excluded, and one duplicate pair filed under two classes was left out entirely. Per class: septoria leaf spot 2,294, leaf blight 1,285, verticulium wilt 772, leaf curl 511, healthy 463. The augmented set is not included. Cashew, cassava and maize can follow in the same project. License: CC BY 4.0. Source: https://data.mendeley.com/datasets/bwh3zbpkpv/1. Please cite: Mensah P.K. et al., CCMT: Dataset for crop pest and disease detection, Data in Brief 49 (2023) 109306.

Cytoplasm-to-nucleus translocation assay from the Broad Bioimage Benchmark Collection (BBBC013v1). 192 fluorescence microscopy images (640 x 640, 8-bit BMP) from one 96-well plate of human U2OS cells: 96 wells x 2 channels (channel 1 FKHR-GFP, channel 2 DNA stained with DRAQ), one field per well. The plate holds four replicate 9-point dose series of the PI3K inhibitors wortmannin (rows A-D, nM) and LY294002 (rows E-H, uM) plus 8 negative-control and 8 positive-control wells (150 nM wortmannin). Ground truth is the platemap: well, drug, dose and control role are stored as file metadata on every image and the control role is also an image-level label (negative-control, positive-control, dose). The two channels of each well are separate files paired through metadata and folder path; there are no cell outlines. License: CC BY 3.0 (Ilya Ravkin). Source: https://bbbc.broadinstitute.org/BBBC013. Please cite: We used image set BBBC013v1 provided by Ilya Ravkin, available from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012].

Astrocyte detection dataset from rat brain tissue (BBBC042, Broad Bioimage Benchmark Collection). 1,117 annotated brightfield microscopy images (990x708) with roughly 15,000 astrocytes labeled as bounding boxes, from the deep-learning astrocyte detection study by Suleymanova et al. License: CC0 1.0 (public domain). Source: https://bbbc.broadinstitute.org/BBBC042. Please cite: Suleymanova I. et al., A deep convolutional neural network approach for astrocyte detection. Scientific Reports 8:12878 (2018), doi 10.1038/s41598-018-31284-x.

Nucleus segmentation dataset of U2OS cells from a chemical screen (BBBC039v1, Broad Bioimage Benchmark Collection). 200 fluorescence microscopy fields (520x696, 16-bit TIFF, Hoechst-stained nuclei) spanning 200 bioactive-compound treatments, with 19,379 individually segmented nucleus instances decoded from the official per-pixel class masks (interior/boundary) and converted to polygon annotations (3 fields have no annotated nuclei). Includes the official train (100) / validation (50) / test (50) split. License: CC0 1.0 (public domain). Source: https://bbbc.broadinstitute.org/BBBC039. Please cite: Caicedo et al. 2018, image set BBBC039v1, available from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012].

Nuclei segmentation dataset from the 2018 Data Science Bowl (BBBC038v1, Broad Bioimage Benchmark Collection). 670 microscopy images with 29,442 individually segmented nuclei as polygon annotations, converted from the official per-nucleus masks. Spans fluorescence and brightfield/histology imagery across multiple organisms and staining protocols. License: CC0 1.0 (public domain). Source: https://bbbc.broadinstitute.org/BBBC038. Please cite: Caicedo et al., Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nature Methods 16, 1247-1253 (2019).

Two-channel cell segmentation dataset of Drosophila melanogaster Kc167 cells (BBBC007v1, Broad Bioimage Benchmark Collection). 16 fields of view, each as a DNA image and an actin image (32 grayscale 8-bit images, 400 to 512 px), with 1,264 hand-outlined nuclei and 1,221 hand-outlined cells converted from the official outline rasters to polygon annotations. The benchmark task is finding the boundaries between touching cells, using the nuclei as seeds. License: CC0 1.0 (public domain). Source: https://bbbc.broadinstitute.org/BBBC007. Please cite: Jones, Carpenter & Golland, Voronoi-based segmentation of cells on image manifolds, Proc. ICCV Workshop on Computer Vision for Biomedical Image Applications (CVBIA), 2005; and Ljosa, Sokolnicki & Carpenter, Annotated high-throughput microscopy image sets for validation, Nature Methods 9, 637 (2012).

Seven-channel iFISH microscopy of human hTERT RPE-1 nuclei from IDR study idr0123 (Mota et al.), complete Dataset 5: 50 fields, each a maximum-intensity projection of the original 61-slice stacks with one page per channel (DAPI plus six DNA FISH probes AF488, AT542, AF594, AT647N, AF700, AF790 marking 23 loci along chromosome 1), 1024x1024, 16-bit, 0.13 um/px. Includes 18,754 published spot coordinates as 9x9 px bounding boxes in six fluorophore classes. Every channel can be shown, hidden, and window-leveled independently in the browser. License: CC BY 4.0. Source and original Z-stacks: https://idr.openmicroscopy.org/study/idr0123/ Please cite: Mota A, Schweitzer M, Wernersson E, Crosetto N, Bienko M. Simultaneous visualization of DNA loci in single cells by combinatorial multi-color iFISH. Scientific Data 9, 47 (2022).