ArticleMolecular biology of the cell2020
AnnotatorJ: an ImageJ plugin to ease hand annotation of cellular compartments.
Article in Molecular biology of the cell, 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.
- SAMJ: fast image annotation on ImageJ/Fiji via segment anything model.Nature communications · 2026Article
- A Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis.Scientific data · 2026Article
- An automated cell-tracking pipeline for the analysis of neutrophil dynamics.Frontiers in bioinformatics · 2026Article
- Live imaging endogenous transcription factor dynamics reveals mechanisms of epiblast and primitive endoderm fate segregation.Current biology : CB · 2025Article
- Advancing open-source visual analytics in digital pathology: A systematic review of tools, trends, and clinical applications.Journal of pathology informatics · 2025Review
- Nuclear instance segmentation and tracking for preimplantation mouse embryos.Development (Cambridge, England) · 2024Article
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.Scientific data · 2024Article
- SuperCUT, an unsupervised multimodal image registration with deep learning for biomedical microscopy.Briefings in bioinformatics · 2024Article
- Comparing Deep Learning Performance for Chronic Lymphocytic Leukaemia Cell Segmentation in Brightfield Microscopy Images.Bioinformatics and biology insights · 2024Article
- Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images.Scientific reports · 2023Article
- Deep Visual Proteomics defines single-cell identity and heterogeneity.Nature biotechnology · 2022Article
- Deep learning for bioimage analysis in developmental biology.Development (Cambridge, England) · 2021Review
- Advances and opportunities in image analysis of bacterial cells and communities.FEMS microbiology reviews · 2021Article
- Segmentor: a tool for manual refinement of 3D microscopy annotations.BMC bioinformatics · 2021Article
- Democratising deep learning for microscopy with ZeroCostDL4Mic.Nature communications · 2021Article
- EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control.Scientific reports · 2021Article
- Neuroinflammatory processes are augmented in mice overexpressing human heat-shock protein B1 following ethanol-induced brain injury.Journal of neuroinflammation · 2021Article
- Fiji plugins for qualitative image annotations: routine analysis and application to image classification.F1000Research · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
AnnotatorJ combines single-cell identification with deep learning (DL) and manual annotation. Cellular analysis quality depends on accurate and reliable detection and segmentation of cells so that the subsequent steps of analyses, for example, expression measurements, may be carried out precisely and without bias. DL has recently become a popular way of segmenting cells, performing unimaginably better than conventional methods. However, such DL applications may be trained on a large amount of annotated data to be able to match the highest expectations. High-quality annotations are unfortunately expensive as they require field experts to create them, and often cannot be shared outside the lab due to medical regulations. We propose AnnotatorJ, an ImageJ plugin for the semiautomatic annotation of cells (or generally, objects of interest) on (not only) microscopy images in 2D that helps find the true contour of individual objects by applying U-Net-based presegmentation. The manual labor of hand annotating cells can be significantly accelerated by using our tool. Thus, it enables users to create such datasets that could potentially increase the accuracy of state-of-the-art solutions, DL or otherwise, when used as training data.
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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.