ArticleCell reports methods2021
A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy.
Article in Cell reports methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 30 citations in OpenAlex.
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- AI-Driven Innovations for Quality Control and Standardization: Future Strategies in Adipose-Derived Stem Cell Manufacturing.International journal of molecular sciences · 2026Review
- Leveraging Vision Transformers for High-Precision Classification of Cancer Cell Cultures: A Comparative Study on MDA-MB-231 and PC3 Datasets.IEEE open journal of engineering in medicine and biology · 2026Article
- Diffraction-informed deep learning for molecular-specific holograms of breast cancer cells.APL bioengineering · 2025Article
- Fine-grained forecasting of COVID-19 trends at the county level in the United States.NPJ digital medicine · 2025Article
- Model-based evaluation of connexin hemichannel permeability.Computational and structural biotechnology journal · 2025Article
- A generative benchmark for evaluating the performance of fluorescent cell image segmentation.Synthetic and systems biotechnology · 2024Article
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Deep learning pipeline for automated cell profiling from cyclic imaging.Scientific reports · 2024Article
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images.Materials today. Bio · 2023Article
- Article
- Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier.Scientific reports · 2023Article
- Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2023Article
- Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance.Artificial intelligence in medicine · 2023Article
- High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury.Scientific reports · 2023Article
- Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images.Scientific reports · 2022Article
- Protocol for live cell image segmentation to profile cellular morphodynamics using MARS-Net.STAR protocols · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
motivationQuantitative studies of cellular morphodynamics rely on extracting leading-edge velocity time series based on accurate cell segmentation from live cell imaging. However, live cell imaging has numerous challenging issues regarding accurate edge localization. Fluorescence live cell imaging produces noisy and low-contrast images due to phototoxicity and photobleaching. While phase contrast microscopy is gentle to live cells, it suffers from the halo and shade-off artifacts that cannot be handled by conventional segmentation algorithms. Here, we present a deep learning-based pipeline, termed MARS-Net (Multiple-microscopy-type-based Accurate and Robust Segmentation Network), that utilizes transfer learning and data from multiple types of microscopy to localize cell edges with high accuracy, allowing quantitative profiling of cellular morphodynamics. SUMMARY: To accurately segment cell edges and quantify cellular morphodynamics from live-cell imaging data, we developed a deep learning-based pipeline termed MARS-Net (multiple-microscopy-type-based accurate and robust segmentation network). MARS-Net utilizes transfer learning and data from multiple types of microscopy to localize cell edges with high accuracy. For effective training on distinct types of live-cell microscopy, MARS-Net comprises a pretrained VGG19 encoder with U-Net decoder and dropout layers. We trained MARS-Net on movies from phase-contrast, spinning-disk confocal, and total internal reflection fluorescence microscopes. MARS-Net produced more accurate edge localization than the neural network models trained with single-microscopy-type datasets. We expect that MARS-Net can accelerate the studies of cellular morphodynamics by providing accurate pixel-level segmentation of complex live-cell datasets.
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.