ReviewJournal of digital imaging2018
Container-Based Clinical Solutions for Portable and Reproducible Image Analysis.
Review in Journal of digital imaging, 2018. 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.
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Recent advances and current landscape of software tools for image analysis and dosimetry in nuclear medicine.EJNMMI physics · 2026Review
- Fully automated on-scanner aortic four-dimensional flow magnetic resonance imaging processing and hemodynamic analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- HiOmics: A cloud-based one-stop platform for the comprehensive analysis of large-scale omics data.Computational and structural biotechnology journal · 2024Article
- SEEG4D: a tool for 4D visualization of stereoelectroencephalography data.Frontiers in neuroinformatics · 2024Article
- AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection.Radiology · 2023Article
- Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3.Human brain mapping · 2023Article
- Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients.Scientific reports · 2023Article
- Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis.Journal of digital imaging · 2022Article
- The Z-Shift: A Need for Quality Management System Level Testing and Standardization in Neuroimaging Pipelines.AJNR. American journal of neuroradiology · 2022Article
- Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays.BMC bioinformatics · 2021Article
- A Serverless Tool for Platform Agnostic Computational Experiment Management.Frontiers in neuroinformatics · 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
6 authors.
Funding
Abstract
Medical imaging analysis depends on the reproducibility of complex computation. Linux containers enable the abstraction, installation, and configuration of environments so that software can be both distributed in self-contained images and used repeatably by tool consumers. While several initiatives in neuroimaging have adopted approaches for creating and sharing more reliable scientific methods and findings, Linux containers are not yet mainstream in clinical settings. We explore related technologies and their efficacy in this setting, highlight important shortcomings, demonstrate a simple use-case, and endorse the use of Linux containers for medical image analysis.
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.