ArticleCell systems2020
nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer.
Article in Cell systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers.
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Who cites it
130 citing papers in PubMed.
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- PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns.bioRxiv : the preprint server for biology · 2026Article
- Prevalent gut phages encode modular adhesins mediating epithelial binding and endoplasmic reticulum trafficking.Nature communications · 2026Article
- Accelerated discovery of cell migration regulators using label-free deep learning-based automated tracking.Science advances · 2026Article
- LBR and LAP2 mediate heterochromatin tethering to the nuclear periphery to preserve genome homeostasis.Nature cell biology · 2026Article
- ROR1-PI3K/AKT signaling drives adaptive resistance to cell cycle blockade in TP53 mutated ovarian cancer.Cell death & disease · 2026Article
- Enhanced Precision of Fluorescence In Situ Hybridization (FISH) Analysis Using Neural Network-Based Nuclear Segmentation for Digital Microscopy Samples.Sensors (Basel, Switzerland) · 2026Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- The evolving landscape of spatial proteomics technologies in the AI age.Fundamental research · 2026Review
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- Tumor cell FAP orchestrates EMT and immune suppression in aggressive localized ccRCC.Theranostics · 2026Article
- Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.PLoS computational biology · 2025Article
- CellSAM: a foundation model for cell segmentation.Nature methods · 2025Article
- Spatial Analysis of the Tumor Microenvironment in Diffuse Large B-cell Lymphoma Reveals Clinically Relevant Cell Interactions and Recurrent Cellular Neighborhoods.Cancer immunology research · 2025Article
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Spatial proteo-transcriptomic profiling reveals the molecular landscape of borderline ovarian tumors and their invasive progression.Cancer cell · 2025Article
- Spiner, deep learning-based automated detection of spiral ganglion neurons in intact cochleae.iScience · 2025Article
- Association of Pretreatment Tumour Microenvironment and Treatment Outcome in Patients With Locally Advanced Melanoma Treated With Isolated Limb Perfusion.Pigment cell & melanoma research · 2025Article
70 more citing papers are in PubMed but not listed here.
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Authors and funding
25 authors.
Funding
Abstract
Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information.
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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.