ArticlePloS one2014
Fast segmentation of stained nuclei in terabyte-scale, time resolved 3D microscopy image stacks.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- Ultrack: pushing the limits of cell tracking across biological scales.Nature methods · 2025Article
- Colorectal cancer histopathology image analysis: A comparative study of prognostic values of automatically extracted morphometric nuclear features in multispectral and red-blue-green imagery.Histology and histopathology · 2024Article
- Large-volume fully automated cell reconstruction generates a cell atlas of plant tissues.The Plant cell · 2024Article
- A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context.Development (Cambridge, England) · 2024Article
- Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets.PLoS computational biology · 2024Article
- Synthesis of large scale 3D microscopic images of 3D cell cultures for training and benchmarking.PloS one · 2023Article
- Using positional information to provide context for biological image analysis with MorphoGraphX 2.0.eLife · 2022Article
- LiveCellMiner: A new tool to analyze mitotic progression.PloS one · 2022Article
- Ventral striatal islands of Calleja neurons control grooming in mice.Nature neuroscience · 2021Article
- hiPSC-Derived Schwann Cells Influence Myogenic Differentiation in Neuromuscular Cocultures.Cells · 2021Article
- Application of 3D MAPs pipeline identifies the morphological sequence chondrocytes undergo and the regulatory role of GDF5 in this process.Nature communications · 2021Article
- Article
- BeadNet: deep learning-based bead detection and counting in low-resolution microscopy images.Bioinformatics (Oxford, England) · 2020Article
- Article
- CytoCensus, mapping cell identity and division in tissues and organs using machine learning.eLife · 2020Article
- Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy.PloS one · 2020Article
- Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates.Computational and structural biotechnology journal · 2020Review
- DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated analysis of multi-dimensional microscopy images has become an integral part of modern research in life science. Most available algorithms that provide sufficient segmentation quality, however, are infeasible for a large amount of data due to their high complexity. In this contribution we present a fast parallelized segmentation method that is especially suited for the extraction of stained nuclei from microscopy images, e.g., of developing zebrafish embryos. The idea is to transform the input image based on gradient and normal directions in the proximity of detected seed points such that it can be handled by straightforward global thresholding like Otsu's method. We evaluate the quality of the obtained segmentation results on a set of real and simulated benchmark images in 2D and 3D and show the algorithm's superior performance compared to other state-of-the-art algorithms. We achieve an up to ten-fold decrease in processing times, allowing us to process large data sets while still providing reasonable segmentation results.
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Registered trials
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.