ArticlePloS one2021
Automatic image annotation for fluorescent cell nuclei segmentation.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prevalence and practices of immunofluorescent cell image processing: a systematic review.Frontiers in cellular neuroscience · 2023Pooled it
- From cells to pixels: A decision tree for designing bioimage analysis pipelines.Journal of microscopy · 2025Article
- A semi-automated algorithm for image analysis of respiratory organoids.PLoS computational biology · 2025Article
- Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies.Communications biology · 2025Article
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- Comparing Deep Learning Performance for Chronic Lymphocytic Leukaemia Cell Segmentation in Brightfield Microscopy Images.Bioinformatics and biology insights · 2024Article
- Towards automatic farrowing monitoring-A Noisy Student approach for improving detection performance of newborn piglets.PloS one · 2024Article
- NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images.Scientific reports · 2023Article
- OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics.PLoS computational biology · 2022Article
- SpotitPy: a semi-automated tool for object-based co-localization of fluorescent labels in microscopy images.BMC bioinformatics · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
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3 authors.
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Abstract
Dataset annotation is a time and labor-intensive task and an integral requirement for training and testing deep learning models. The segmentation of images in life science microscopy requires annotated image datasets for object detection tasks such as instance segmentation. Although the amount of annotated image data has been steadily reduced due to methods such as data augmentation, the process of manual or semi-automated data annotation is the most labor and cost intensive task in the process of cell nuclei segmentation with deep neural networks. In this work we propose a system to fully automate the annotation process of a custom fluorescent cell nuclei image dataset. By that we are able to reduce nuclei labelling time by up to 99.5%. The output of our system provides high quality training data for machine learning applications to identify the position of cell nuclei in microscopy images. Our experiments have shown that the automatically annotated dataset provides coequal segmentation performance compared to manual data annotation. In addition, we show that our system enables a single workflow from raw data input to desired nuclei segmentation and tracking results without relying on pre-trained models or third-party training datasets for neural networks.
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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.