Cell Segmentation

The STARE (Structured Analysis of the Retina) dataset is a dataset for retinal vessel segmentation. It contains 20 equal-sized (700×605) color fundus images. For each image, two groups of annotations are provided.. Source: DPN: Detail-Preserving Network with High Resolution Representation for Efficient Segmentation of Retinal Vessels Image Source: https://www.researchgate.net/figure/Results-of-the-different-methods-applied-to-the-STARE-dataset-a-original-image-b_fig4_279215756

The colorectal nuclear segmentation and phenotypes (CoNSeP) dataset consists of 41 H&E stained image tiles, each of size 1,000×1,000 pixels at 40× objective magnification. The images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs, each belonging to an individual patient, and scanned with an Omnyx VL120 scanner within the department of pathology at University Hospitals Coventry and Warwickshire, UK. Source: HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images Image Source: Graham et al

PanNuke is a semi automatically generated nuclei instance segmentation and classification dataset with exhaustive nuclei labels across 19 different tissue types. The dataset consists of 481 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the dataset contains 205,343 labeled nuclei, each with an instance segmentation mask. Source: PanNuke Dataset Extension, Insights and Baselines Image Source: https://jgamper.github.io/PanNukeDataset/

Introduced by Da et al. in DigestPath: a Benchmark Dataset with Challenge Review for the Pathological Detection and Segmentation of Digestive-System ### Grand-Challenge Page ### 1. Signet ring cell dataset Signet ring cell carcinoma is a type of rare adenocarcinoma with poor prognosis. Early detection of such cells leads to huge improvement of patients' survival rate. However, there is no existing public dataset with annotations for studying the problem of signet ring cell detection. This dataset has positive samples and negative samples. Training positive samples contain 77 images from 20 WSIs, with cell bounding boxes written in xml. Training negative samples contain 378 images from 79 WSIs.These negative WSIs have no signet ring, but could contain other kinds of tumor cells. Each signet ring cell is labeled by experienced pathologists with a rectangle bounding box tightly surrounding the cell. Each image is of size 2000X2000. The training images are from 2 organs, including gastric mucosa and intestine. Because of the difficulty of manual annotation, there exist some signet ring cells who are missed by pathologists. In other words, this dataset is a noisy dataset with its positive images not fully annotated. All whole slide images were stained by hematoxylin and eosin and scanned at X40. ### 2. Colonoscopy tissue segment dataset Colonoscopy pathology examination can find cells of early-stage colon tumor from small tissue slices. Pathologists need to daily examine hundreds of tissue slices, which is a time consuming and exhausting work. Here we propose a challenge task on automatic colonoscopy tissue segmentation and screening, aiming at automatic lesion segmentation and classification of the whole tissue (benign vs. malignant). This dataset has positive samples and negative samples. Training positive samples contain 250 images of tissue from 93 WSIs, with pixel-level annotation in jpg format, where 0 means background and 255 for foreground (malignant lesion). You could simply get binary mask by a threshold 128. Training negative samples contain 410 images of tissue from 231 WSI. This negative images have no annotation because they don't have any malignant lesion. The average size of all images are of 5000x5000 pixels, some of them are extremely huge. We will also provide another 152 patients' 212 tissues as the testing set, in which 90 images from 65 patients contain lesion. All whole slide images were stained by hematoxylin and eosin and scanned at X20. Sign the DATABASE USE AGREEMENT first and download the dataset at the homepage! Da Q, Huang X, Li Z, et al. DigestPath: a Benchmark Dataset with Challenge Review for the Pathological Detection and Segmentation of Digestive-System[J]. Medical Image Analysis, 2022: 102485. （https://doi.org/10.1016/j.media.2022.102485）

The LIVECell (Label-free In Vitro image Examples of Cells) dataset is a large-scale microscopic image dataset for instance-segmentation of individual cells in 2D cell cultures. LIVECell consists of 5,239 manually annotated, expert-validated, Incucyte HD phase-contrast microscopy images with a total of 1,686,352 individual cells annotated from eight different cell types (average 313 cells per image). The LIVECell images have predefined splits into training (3188), validation (539) and test (1512) sets. Each split is also further subdivided into each of the eight cell types. The training set also has splits of different sizes (2, 4, 5, 25, 50%) to allow dataset size experimentation.

The dataset for this challenge was obtained by carefully annotating tissue images of several patients with tumors of different organs and who were diagnosed at multiple hospitals. This dataset was created by downloading H&E stained tissue images captured at 40x magnification from TCGA archive. H&E staining is a routine protocol to enhance the contrast of a tissue section and is commonly used for tumor assessment (grading, staging, etc.). Given the diversity of nuclei appearances across multiple organs and patients, and the richness of staining protocols adopted at multiple hospitals, the training datatset will enable the development of robust and generalizable nuclei segmentation techniques that will work right out of the box. Source: MoNuSeg

GFP-GOWT1 mouse stem cells Dr. E. Bártová. Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic

HeLa cells stably expressing H2b-GFP Mitocheck Consortium

The archive contains original images from U2OS cells stained with Hoechst 33342 as PNG files. It also contains images (as Photoshop and GIMP files) showing hand-segmentation of the Hoechst images into regions containing single nuclei.

Deep learning use for quantitative image analysis is exponentially increasing. However, training accurate, widely deployable deep learning algorithms requires a plethora of annotated (ground truth) data. Image collections must contain not only thousands of images to provide sufficient example objects (i.e. cells), but also contain an adequate degree of image heterogeneity. We present a new dataset, EVICAN-Expert visual cell annotation, comprising partially annotated grayscale images of 30 different cell lines from multiple microscopes, contrast mechanisms and magnifications that is readily usable as training data for computer vision applications. With 4600 images and ∼26 000 segmented cells, our collection offers an unparalleled heterogeneous training dataset for cell biology deep learning application development. The dataset is freely available (https://edmond.mpdl.mpg.de/imeji/collection/l45s16atmi6Aa4sI?q=).

The archive contains original images from NIH3T3 cells stained with Hoechst 33342 as PNG files. It also contains images (as Photoshop and GIMP files) showing hand-segmentation of the Hoechst images into regions containing single nuclei.

Glioblastoma-astrocytoma U373 cells on a polyacrylamide substrate Dr. S. Kumar. Department of Bioengineering, University of California at Berkeley, Berkeley CA (USA)

This dataset is a collection of fluorescent images from mice in order to test an automatic cell counting tool that we developed. 62 images viewed from 2 or 3 different fields of views are shown. In brief, the dataset was derived from brain sections of a model for HIV-induced brain injury (HIVgp120tg), which expresses soluble gp120 envelope protein in astrocytes under the control of a modified GFAP promoter. The mice were in a mixed C57BL/6.129/SJL genetic background, and two genotypes of 9 month old male mice were selected: wild type controls (Resting, n = 3) and transgenic littermates (HIVgp120tg, Activated, n = 3). No randomization was performed. HIVgp120tg mice show among other hallmarks of human HIV neuropathology an increase in microglia numbers which indicates activation of the cells compared to non-transgenic littermate controls. Brain sections were obtained using a vibratome (Leica VT1000S, Leica Biosystems, Buffalo Grove, IL) and cerebral cortex in 40 μm thick sagittal sections spaced 320 μm apart medial to lateral from brains of each genotype. Staining was performed with rabbit anti-ionized calcium-binding adaptor molecule 1 (Iba-1) IgG (1:125; Wako) with secondary antibody Fluorescein isothiocyanate (FITC). For quantification of Iba-1 stained microglia, cell bodies were counted in the cerebral cortex from three fields of view for three sections each per animal. Between 2 and 3 images were collected per field of view to capture as many cells as possible in sufficient focus for identification. Images were acquired at 10X magnification and pixel resolution 1280x1280 and cropped to 1280x733 pixel area to exclude irregular tissue edges. For more details, please refer to ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation. We note that this data repository links to some images gathered in the fluocells (Fluorescent Neuronal Cells) dataset introduced by Morelli et al. which can be found here: https://paperswithcode.com/dataset/fluocells. We provide a link to our automatic cell counting tool that this dataset was used for here at the following Github link: https://github.com/tkataras/Automatic-Cell-Counting-with-TWS.

HeLa cells on a flat glass Dr. G. van Cappellen. Erasmus Medical Center, Rotterdam, The Netherlands

MDA231 human breast carcinoma cells infected with a pMSCV vector including the GFP sequence, embedded in a collagen matrix Dr. R. Kamm. Dept. of Biological Engineering, Massachusetts Institute of Technology, Cambridge MA (USA)

Simulated nuclei of HL60 cells stained with Hoescht Dr. V. Ulman and Dr. D. Svoboda. Centre for Biomedical Image Analysis (CBIA), Masaryk University, Brno, Czech Republic (Created using MitoGen, part of Cytopacq)

We introduce the trapped yeast cell (TYC) dataset, a novel dataset for understanding instance-level semantics and motions of cells in microstructures. We release $105$ dense annotated high-resolution brightfield microscopy images, including about $19$k instance masks. We also release $261$ curated video clips composed of $1293$ high-resolution microscopy images to facilitate unsupervised understanding of cell motions and morphology.

Microscopy is a cornerstone of biomedical research, enabling detailed study of biological structures at multiple scales. Advances in cryo-electron microscopy, high-throughput fluorescence microscopy, and whole-slide imaging allow the rapid generation of terabytes of image data, which are essential for fields such as cell biology, biomedical research, and pathology. These data span multiple scales, allowing researchers to examine atomic/molecular, subcellular/cellular, and cell/tissue-level structures with high precision. A crucial first step in microscopy analysis is interpreting and reasoning about the significance of image findings. This requires domain expertise and comprehensive knowledge of biology, normal/abnormal states, and the capabilities and limitations of microscopy techniques. Vision-language models (VLMs) offer a promising solution for large-scale biological image analysis, enhancing researchers’ efficiency, identifying new image biomarkers, and accelerating hypothesis generation and scientific discovery. However, there is a lack of standardized, diverse, and large-scale vision-language benchmarks to evaluate VLMs’ perception and cognition capabilities in biological image understanding. To address this gap, we introduce μ-Bench, an expert-curated benchmark encompassing 22 biomedical tasks across various scientific disciplines (biology, pathology), microscopy modalities (electron, fluorescence, light), scales (subcellular, cellular, tissue), and organisms in both normal and abnormal states. We evaluate state-of-the-art biomedical, pathology, and general VLMs on μ-Bench and find that: i) current models struggle in all categories, even for basic tasks such as distinguishing microscopy modalities; ii) current specialist models fine-tuned on biomedical data often perform worse than generalist models; iii) fine-tuning in specific microscopy domains can cause catastrophic forgetting, eroding prior biomedical knowledge encoded in their base model. iv) weight interpolation between fine-tuned and pre-trained models offers one solution to forgetting and improves general performance across biomedical tasks. We release μ-Bench under a permissive license to accelerate the research and development of microscopy foundation models.

An instance segmentation dataset of yeast cells in microstructures. The dataset includes 493 densely annotated microscopy images. For more information see the paper "An Instance Segmentation Dataset of Yeast Cells in Microstructures".

ALFI (Annotations for Label-Free Images) is a dataset of images and annotations for label-free microscopy imaging. It consists of 29 time-lapse image sequences with various annotations (pixel-wise segmentation masks, object-wise bounding boxes, and tracking information), made publicly available to the scientific community through figshare. For a detailed description, see "L. Antonelli, F. Polverino, A. Albu, A. Hada, I.A. Asteriti, F. Degrassi, G. Guarguaglini, L. Maddalena, M.R. Guarracino, ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells, Scientific Data 10(677), 2023". https://www.nature.com/articles/s41597-023-02540-1

Simulated GFP-actin-stained A549 Lung Cancer cells embedded in a Matrigel matrix Dr. M. Maška and Dr. D. V. Sorokin. Centre for Biomedical Image Analysis (CBIA), Masaryk University, Brno, Czech Republic (Created using FiloGen, part of Cytopacq)

Developing Tribolium Castaneum embryo (3D cartographic projection) Dr. A. Jain. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany