ArticleBioinformatics (Oxford, England)2020
EVICAN-a balanced dataset for algorithm development in cell and nucleus segmentation.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Microscopy Cell Segmentation: Review and Benchmarking of Task-Specific and Foundation Models.Journal of imaging · 2026Review
- How to Train Custom Cell Segmentation Models Using Cell-APP.Bio-protocol · 2026Article
- MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis.Nature methods · 2025Review
- Cell-APP: A generalizable method for cell annotation and cell-segmentation model training.Molecular biology of the cell · 2025Article
- Cell-APP: A generalizable method for cell annotation and cell-segmentation model training.bioRxiv : the preprint server for biology · 2025Article
- Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models.Diagnostics (Basel, Switzerland) · 2025Article
- Comparative Study of Deep Transfer Learning Models for Semantic Segmentation of Human Mesenchymal Stem Cell Micrographs.International journal of molecular sciences · 2025Article
- Enhanced stem cell image segmentation by leveraging visual processing mechanisms.Frontiers in bioengineering and biotechnology · 2025Article
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- CMTT-JTracker: a fully test-time adaptive framework serving automated cell lineage construction.Briefings in bioinformatics · 2024Article
- Instance segmentation of cells and nuclei from multi-organ cross-protocol microscopic images.Quantitative imaging in medicine and surgery · 2024Article
- Automated segmentation and recognition of C. elegans whole-body cells.Bioinformatics (Oxford, England) · 2024Article
- ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells.Scientific data · 2023Article
- Article
- Label-free tumor cells classification using deep learning and high-content imaging.Scientific data · 2023Article
- Brightfield vs Fluorescent Staining Dataset-A Test Bed Image Set for Machine Learning based Virtual Staining.Scientific data · 2023Article
- A novel feature for monitoring the enzymatic harvesting process of adherent cell cultures based on lens-free imaging.Scientific reports · 2022Article
- UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues.Communications biology · 2022Article
- Self-supervised machine learning for live cell imagery segmentation.Communications biology · 2022Article
- Next-Generation Pathology Using Multiplexed Immunohistochemistry: Mapping Tissue Architecture at Single-Cell Level.Frontiers in oncology · 2022Review
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3 authors.
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Abstract
motivationDeep 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.
resultsWe 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. AVAILABILITY AND IMPLEMENTATION: The dataset is freely available (https://edmond.mpdl.mpg.de/imeji/collection/l45s16atmi6Aa4sI?q=). Using a Mask R-CNN implementation, we demonstrate automated segmentation of cells and nuclei from brightfield images with a mean average precision of 61.6 % at a Jaccard Index above 0.5.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.