Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s10278-018-0089-4 | J. Digital Imaging |
Keywords | Field | DocType |
Containers,Docker,Medical-imaging,Reproducibility,Singularity | Computer vision,Abstraction,Medical imaging,Computer science,Human–computer interaction,Software,Artificial intelligence,Computation,Scientific method | Journal |
Volume | Issue | ISSN |
31 | 3 | 0897-1889 |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jordan Matelsky | 1 | 1 | 0.35 |
| Gregory Kiar | 2 | 14 | 2.28 |
| Erik Johnson | 3 | 1 | 0.35 |
| Corban G. Rivera | 4 | 41 | 3.19 |
| Michael Toma | 5 | 1 | 0.35 |
| William Gray Roncal | 6 | 38 | 8.25 |