State of the Practice for Medical Imaging Software Based on Open Source Repositories
Bibliographic record
Abstract
We review the state of the practice for the development of medical imaging (MI) software based on data available in open-source repositories. We selected 29 projects from 48 candidates and assessed nine software qualities by answering 108 questions for each. Using the analytic hierarchy process (AHP) on the quantitative data, we ranked the MI software. The top five are 3D Slicer, ImageJ, Fiji, OHIF Viewer, and ParaView. This is consistent with the community's view, with four of these also appearing in the top five using GitHub metrics (stars per year). The quality and quantity of documentation present in a project correlate quite well with its popularity. Generally, MI software is in a healthy state: in the repositories, we observed 88% of the documentation artifacts recommended by research software development guidelines, and 100% of MI projects use version control tools. However, the current state of the practice deviates from existing guidelines as some recommended artifacts are rarely present (such as a test plan, requirements' specification, and code style guidelines), low usage of continuous integration (17% of the projects), low use of unit testing (~ 50% of projects), and room for improvement with documentation. From developer interviews, we identified seven concerns: lack of development time, lack of funding, technology hurdles, correctness, usability, maintainability, and reproducibility. We recommend increasing effort on documentation, increasing testing by enriching datasets, increasing continuous integration, moving to web applications, employing linters, using peer reviews, and designing for change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.143 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.034 | 0.021 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".