ArticleScientific data2020
An annotated fluorescence image dataset for training nuclear segmentation methods.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 81 citations in OpenAlex.
- High-frequency energy fusion (HFEF) for nuclei segmentation with boundary-aware loss.Quantitative imaging in medicine and surgery · 2026Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis.Nature methods · 2025Review
- Deep Disentangled Representation Learning Reveals Neuron Subtype-Specific Nuclear Morphologies Across Aging in Mice and Humans.bioRxiv : the preprint server for biology · 2025Article
- Antibody-Based Multiplex Image Analysis: Standard Analytical Workflows and Artificial Intelligence Tools for Pathologists.Laboratory investigation; a journal of technical methods and pathology · 2025Review
- Image-based drug screening combined with molecular profiling identifies signatures and drivers of therapy resistance in pediatric AML.Cell reports. Medicine · 2025Article
- LOGSAGE: LOG-BASED SALIENCY FOR GUIDED ENCODING IN ROBUST NUCLEI SEGMENTATION OF IMMUNOFLUORESCENCE HISTOLOGY IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- CellBinDB: a large-scale multimodal annotated dataset for cell segmentation with benchmarking of universal models.GigaScience · 2025Article
- AneRBC dataset: a benchmark dataset for computer-aided anemia diagnosis using RBC images.Database : the journal of biological databases and curation · 2024Article
- State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues.Journal of imaging · 2024Review
- Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques.Diagnostics (Basel, Switzerland) · 2024Review
- BIOMERO: A scalable and extensible image analysis framework.Patterns (New York, N.Y.) · 2024Article
- DLSIA: Deep Learning for Scientific Image Analysis.Journal of applied crystallography · 2024Article
- NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.Scientific data · 2024Article
- Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.Scientific data · 2024Article
- Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue.Scientific reports · 2024Article
- Evaluation of cell segmentation methods without reference segmentations.Molecular biology of the cell · 2023Article
- NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation.Sensors (Basel, Switzerland) · 2023Article
- Transgenic HPV11-E2 protein modulates URR activity in vivo.Transgenic research · 2023Article
- A survey on recent trends in deep learning for nucleus segmentation from histopathology images.Evolving systems · 2023Review
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 5 countries.
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Abstract
Fully-automated nuclear image segmentation is the prerequisite to ensure statistically significant, quantitative analyses of tissue preparations,applied in digital pathology or quantitative microscopy. The design of segmentation methods that work independently of the tissue type or preparation is complex, due to variations in nuclear morphology, staining intensity, cell density and nuclei aggregations. Machine learning-based segmentation methods can overcome these challenges, however high quality expert-annotated images are required for training. Currently, the limited number of annotated fluorescence image datasets publicly available do not cover a broad range of tissues and preparations. We present a comprehensive, annotated dataset including tightly aggregated nuclei of multiple tissues for the training of machine learning-based nuclear segmentation algorithms. The proposed dataset covers sample preparation methods frequently used in quantitative immunofluorescence microscopy. We demonstrate the heterogeneity of the dataset with respect to multiple parameters such as magnification, modality, signal-to-noise ratio and diagnosis. Based on a suggested split into training and test sets and additional single-nuclei expert annotations, machine learning-based image segmentation methods can be trained and evaluated.
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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.