ArticleScientific reports2016
Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 76 citations in OpenAlex.
- Weak-to-strong generalization enables fully automated training of multi-head mask-RCNN model for segmenting densely overlapping cell nuclei in multiplex whole-slice brain images.Frontiers in bioinformatics · 2026Article
- Automated detection of c-Fos-expressing neurons using inhomogeneous background subtraction in fluorescent images.Neurobiology of learning and memory · 2025Article
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- 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
- Fisheye transformation enhances deep-learning-based single-cell phenotyping by including cellular microenvironment.Cell reports methods · 2022Article
- Human lung-cancer-cell radioresistance investigated through 2D network topology.Scientific reports · 2022Article
- Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei.BMC bioinformatics · 2022Article
- Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer.PloS one · 2021Article
- Graph-Theoretic Post-Processing of Segmentation With Application to Dense Biofilms.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2021Article
- Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation.Nature communications · 2020Article
- Quantitative methods in microscopy to assess pollen viability in different plant taxa.Plant reproduction · 2020Article
- ShapeMetrics: A userfriendly pipeline for 3D cell segmentation and spatial tissue analysis.Developmental biology · 2020Article
- nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer.Cell systems · 2020Article
- Discrimination and quantification of live/dead rat brain cells using a non-linear segmentation model.Medical & biological engineering & computing · 2020Article
- Test-time augmentation for deep learning-based cell segmentation on microscopy images.Scientific reports · 2020Article
- Accurate Neuronal Soma Segmentation Using 3D Multi-Task Learning U-Shaped Fully Convolutional Neural Networks.Frontiers in neuroanatomy · 2020Article
- DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data.Scientific reports · 2019Article
- Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2019Article
- Multidimensional profiling of drug-treated cells by Imaging Mass Cytometry.FEBS open bio · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of fluorescently stained cell nuclei is the basis of cell detection, segmentation, and feature extraction in high content microscopy experiments. The nuclear morphology of single cells is also one of the essential indicators of phenotypic variation. However, the cells used in experiments can lose their contact inhibition, and can therefore pile up on top of each other, making the detection of single cells extremely challenging using current segmentation methods. The model we present here can detect cell nuclei and their morphology even in high-confluency cell cultures with many overlapping cell nuclei. We combine the "gas of near circles" active contour model, which favors circular shapes but allows slight variations around them, with a new data model. This captures a common property of many microscopic imaging techniques: the intensities from superposed nuclei are additive, so that two overlapping nuclei, for example, have a total intensity that is approximately double the intensity of a single nucleus. We demonstrate the power of our method on microscopic images of cells, comparing the results with those obtained from a widely used approach, and with manual image segmentations by experts.
Indexed as
Identifiers
What OpenQuestion holds
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.