ArticleiScience2022
New open-source software for subcellular segmentation and analysis of spatiotemporal fluorescence signals using deep learning.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Neuronal regulation of myenteric interstitial cells of Cajal (ICC-MY) in the proximal colon.Cell calcium · 2025Article
- A Semi-Automatic Tool for the Standardized Analysis of Fluorescent Intensity Changes in Polarized Cells.International journal of molecular sciences · 2025Article
- Ca²⁺ signaling in myenteric interstitial cells of Cajal (ICC-MY) and their role as conditional pacemakers in the colon.Cell calcium · 2025Article
- Novel imaging approach for simultaneous tracking of cell dynamics in distinct tissue layers reveals cells involved in colonic peristalsis.Frontiers in imaging (Lausanne, Switzerland) · 2025Article
- Article
- SANS-CNN: An automated machine learning technique for spaceflight associated neuro-ocular syndrome with astronaut imaging data.NPJ microgravity · 2024Article
- CaPhysiological reviews · 2024Review
- Live-cell imaging in the deep learning era.Current opinion in cell biology · 2023Review
- Machine learning (ML)-assisted surface tension and oscillation-induced elastic modulus studies of oxide-coated liquid metal (LM) alloys.JPhys materials · 2023Article
- Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence.Annals of biomedical engineering · 2023Review
- Article
- Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks.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
8 authors.
Funding
Abstract
Cellular imaging instrumentation advancements as well as readily available optogenetic and fluorescence sensors have yielded a profound need for fast, accurate, and standardized analysis. Deep-learning architectures have revolutionized the field of biomedical image analysis and have achieved state-of-the-art accuracy. Despite these advancements, deep learning architectures for the segmentation of subcellular fluorescence signals is lacking. Cellular dynamic fluorescence signals can be plotted and visualized using spatiotemporal maps (STMaps), and currently their segmentation and quantification are hindered by slow workflow speed and lack of accuracy, especially for large datasets. In this study, we provide a software tool that utilizes a deep-learning methodology to fundamentally overcome signal segmentation challenges. The software framework demonstrates highly optimized and accurate calcium signal segmentation and provides a fast analysis pipeline that can accommodate different patterns of signals across multiple cell types. The software allows seamless data accessibility, quantification, and graphical visualization and enables large dataset analysis throughput.
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.