ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2013
A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Quantifying Nuclear Structures of Digital Pathology Images Across Cancers Using Transport-Based Morphometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2025Article
- Segmentation of Multiple Nuclei from Non-overlapping Immuno-histochemically Stained Histological Hepatic Images.Journal of digital imaging · 2023Article
- Neuroblastoma GD2 Expression and Computational Analysis of Aptamer-Based Bioaffinity Targeting.International journal of molecular sciences · 2021Review
- Automatic segmentation of skin cells in multiphoton data using multi-stage merging.Scientific reports · 2021Article
- Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions.Journal of thyroid research · 2020Article
- A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification.International journal of molecular sciences · 2019Review
- Robust Cell Detection for Large-Scale 3D Microscopy Using GPU-Accelerated Iterative Voting.Frontiers in neuroanatomy · 2018Article
- Spatial Statistics for Segmenting Histological Structures in H&E Stained Tissue Images.IEEE transactions on medical imaging · 2017Article
- Predictive Nuclear Chromatin Characteristics of Melanoma and Dysplastic Nevi.Journal of pathology informatics · 2017Article
- Large-scale localization of touching somas from 3D images using density-peak clustering.BMC bioinformatics · 2016Article
- Joint level-set and spatio-temporal motion detection for cell segmentation.BMC medical genomics · 2016Article
- Automatic screening of cervical cells using block image processing.Biomedical engineering online · 2016Article
- Detecting and segmenting cell nuclei in two-dimensional microscopy images.Journal of pathology informatics · 2016Article
- Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review.IEEE reviews in biomedical engineering · 2016Review
- Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future.Biomedical engineering and computational biology · 2016Review
- Towards automatic image analysis and assessment of the multicellular apoptosis process.IET image processing · 2015Article
- Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2015Article
- Detection of malignant mesothelioma using nuclear structure of mesothelial cells in effusion cytology specimens.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2015Article
- Data cluster analysis-based classification of overlapping nuclei in Pap smear samples.Biomedical engineering online · 2014Article
- Accurate diagnosis of thyroid follicular lesions from nuclear morphology using supervised learning.Medical image analysis · 2014Article
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4 authors.
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Abstract
We describe a new supervised learning-based template matching approach for segmenting cell nuclei from microscopy images. The method uses examples selected by a user for building a statistical model that captures the texture and shape variations of the nuclear structures from a given dataset to be segmented. Segmentation of subsequent, unlabeled, images is then performed by finding the model instance that best matches (in the normalized cross correlation sense) local neighborhood in the input image. We demonstrate the application of our method to segmenting nuclei from a variety of imaging modalities, and quantitatively compare our results to several other methods. Quantitative results using both simulated and real image data show that, while certain methods may work well for certain imaging modalities, our software is able to obtain high accuracy across several imaging modalities studied. Results also demonstrate that, relative to several existing methods, the template-based method we propose presents increased robustness in the sense of better handling variations in illumination, variations in texture from different imaging modalities, providing more smooth and accurate segmentation borders, as well as handling better cluttered nuclei.
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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.