--- name: ieee-transactions-on-medical-imaging description: Use when targeting IEEE Transactions on Medical Imaging (TMI) or deciding whether a medical-imaging methods manuscript fits this venue. Encodes the journal's fit, the imaging-specific-method-with-proper-validation bar, dataset and evaluation rigor, house style, official-submission re-check, and desk-reject heuristics. --- # IEEE Transactions on Medical Imaging (ieee-transactions-on-medical-imaging) ## Journal positioning IEEE Transactions on Medical Imaging (TMI), published jointly by several IEEE societies, is a flagship archival venue for **methods in medical image formation, reconstruction, and analysis** across modalities (MRI, CT, PET/SPECT, ultrasound, optical, and microscopy), including registration, segmentation, quantification, and machine learning for medical imaging. The defining expectation is a method whose contribution is **specific to medical imaging** and validated with appropriate data and proper technical and, where relevant, clinical evaluation — not a generic computer-vision method run on a medical dataset as an afterthought. Papers without medical-imaging specificity, or evaluated on tiny/unrepresentative data without rigorous protocol, are a poor fit. This skill is a **fit / venue-selection / re-framing** tool. It does not replace the journal's current official author information. Before submitting, re-check the live IEEE TMI author guidance and submission system. ## When to trigger - The author names TMI for an image reconstruction, registration, segmentation, or imaging-ML manuscript and wants a fit/framing check. - A method must be re-framed so the **medical-imaging-specific** contribution — the physics, the modality, or the clinical task — is central, not a generic CV result. - The author is unsure whether the contribution belongs in TMI (imaging methods) or a broader biomedical/translation venue. - The author needs TMI's dataset-and-validation bar and desk-reject heuristics. ## Scope & topic fit - Image formation and reconstruction: inverse problems and model-based or learning-based reconstruction for MRI, CT, PET/SPECT, ultrasound, and optical imaging. - Image analysis: segmentation, registration, detection, and quantification with a medical-imaging-specific methodological advance. - Machine learning for medical imaging when the method addresses imaging-specific challenges (modality physics, limited/heterogeneous labels, domain shift, artifacts). - Quantitative imaging, biomarker extraction, and motion/artifact correction tied to a defined imaging or clinical task. - Imaging-system and acquisition methods (sampling, hardware-aware reconstruction) evaluated on realistic or measured data. - Computational/physics models of image formation validated against acquired data. ## Method & evidence bar - The contribution must be **imaging-specific**: exploit modality physics, acquisition model, or clinical task; a generic network applied to images does not clear the bar. - Validation must use appropriate datasets with adequate size and diversity; report data source, acquisition, ground-truth/reference standard, and any patient/ethics provenance. - Evaluation must use task-appropriate metrics with statistics: reconstruction fidelity, segmentation overlap/boundary error, registration accuracy, detection performance — with confidence intervals or significance where claimed. - Compare against the right baselines (established imaging methods, not only one CV model), with matched preprocessing and fair tuning; ablate key components. - Address generalization and robustness: cross-site/scanner/protocol variation, out-of-distribution behavior, and failure modes relevant to clinical use. - Reproducibility: enough detail (and ideally code and data access per policy) to reproduce the reported results. ## Structure & house style - IEEE double-column format; TMI publishes full-length **Papers** — match the contribution to that archival scope and re-check current article types and length policy on the live guide. - The introduction motivates the imaging/clinical gap and the methodological need, then states the contribution; avoid framing it as a generic-CV improvement. - Figures are load-bearing: example images with the relevant overlays, quantitative comparison plots, and failure cases; include clinically meaningful visualizations. - The methods section must specify the imaging model, data, and evaluation protocol precisely enough to reproduce. - A results section with quantitative tables across datasets and baselines is central. ## Official-submission checklist - Before giving submission-ready advice, read `../../resources/source-basis.md` and `../../resources/official-source-map.md`; start from the IEEE Author Center anchors, then cite the current TMI-specific page you checked. - Search the live site for "IEEE Transactions on Medical Imaging information for authors" and follow the current ScholarOne/IEEE version. - Re-check article types, page/length limits and overlength policy, and the IEEE double-column template. - Confirm data/code-availability, human-subjects/ethics/IRB, and any de-identification and reporting requirements. - Re-check ORCID, competing-interests, funding, author-contribution, and AI-use disclosure requirements, and IEEE open-access options. - If the live official instructions conflict with this skill, the official instructions win. ## Pre-submission self-check - [ ] The contribution is medical-imaging-specific (physics/modality/clinical task), not a generic CV method on medical data. - [ ] Validation uses appropriate, adequately sized and diverse datasets with documented reference standards. - [ ] Metrics are task-appropriate and reported with statistics; baselines are the right imaging methods. - [ ] Generalization across site/scanner/protocol and failure modes are addressed. - [ ] Ethics/IRB and data provenance/de-identification are documented. - [ ] Article type and length fit current TMI limits; methods are reproducible. ## Common desk-reject triggers - A generic computer-vision/deep-learning method with no medical-imaging-specific contribution. - Evaluation on a tiny, single-site, or unrepresentative dataset with no rigorous protocol. - Missing or inappropriate baselines; unfair comparisons or no ablation of the claimed novelty. - No ethics/IRB statement or data provenance for human-subject imaging data. - Clinical-utility claims with no clinically meaningful validation or appropriate reference standard. ## Re-routing decision - Broader biomedical engineering (devices, biosignals, non-imaging) → `ieee-transactions-on-biomedical-engineering`. - Highest-significance clinical translation/impact story → `nature-biomedical-engineering`. - Core contribution is a general signal-processing method → `ieee-transactions-on-signal-processing`. - Surgical/medical-robotics contribution as the core → `ieee-transactions-on-robotics`. - General computer-vision advance with no medical specificity → a computer-vision venue. ## Output format ```text [Fit] High / Medium / Low (one-line reason) [Target] IEEE Transactions on Medical Imaging [Topic tags] <2–3 closest medical-imaging subtopics> [Imaging-specific contribution] [Method/evidence] [Top risk] [Article type] Paper [Official items to re-check]
[Re-route suggestion] ```