The Tool Desk
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Start by defining the intended use
Validation evidence applies only to the use that was actually studied. Write down what the segmentation is meant to do and where it fits in care before choosing cases, readers, or metrics.
- Purpose and output: Identify the structure or lesion to be segmented and whether the output supports radiotherapy planning, lesion measurement, surgical planning, or another task.
- Population and inputs: Specify the intended patient population, anatomy, imaging modality, acquisition protocols, and relevant disease or image-quality variation.
- User and workflow: State who receives the contour, at what point in care, and whether the system acts autonomously, provides a draft for editing, or serves as a measurement aid.
- Consequences and safeguards: Describe what could happen if the contour is missing, delayed, over-segmented, or under-segmented. Define when a user should correct, override, or escalate an output.
These details determine what counts as a consequential error. A contour acceptable as a clinician-edited starting point may not be adequate for an autonomous task, and a small boundary displacement may matter more for one application than another.
Design the evaluation set before examining results
Use a test set that reflects the intended population and use, and keep it separate from data used to train or tune the system. A large set is not automatically representative: its composition must support the populations, sites, and conditions covered by the intended claim.
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Cover expected variation
Include relevant clinical sites, scanners, acquisition protocols, image quality, disease severity, and anatomical variation. Consider demographic and clinical subgroups that could affect performance or the consequences of errors. If deployment will span distinct sites or acquisition conditions, include data that reflects those conditions and, where feasible, comes from sites not used for development.
Prespecify the evaluation plan
Before running the locked system on the test set, define inclusion and exclusion criteria, handling of missing or corrupted inputs, primary and secondary measures, subgroup analyses, and statistical methods. Set criteria for failures and stopping or escalation in advance. Report the actual composition of the test set so readers can judge whether it supports the intended use.
Make the reference contours transparent
An expert-drawn contour is an estimate, not necessarily an error-free ground truth. FDA notes that expert-defined labels can have substantial variability or uncertainty. Document how the reference was produced so agreement scores can be interpreted in light of that uncertainty.
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- Record reader qualifications and experience, annotation instructions, tools, and whether readers were blinded to the AI output or to one another.
- Describe how ambiguous boundaries were handled and whether contours were reviewed, adjudicated, or combined into a consensus reference.
- When feasible, retain individual expert contours as well as any adjudicated contour. This makes it possible to examine reader-to-reader variation rather than hiding it inside a single reference.
- Explain why the chosen reference approach fits the clinical task. A single reader, a panel, and an adjudicated contour each represent different ways of estimating the target.
Choose measures for the errors that could change care
Use measures that match the application, output, and data structure; FDA’s performance-assessment work cautions against treating metric selection as one-size-fits-all. Prespecify the measures and any acceptance criteria, and explain why they reflect the intended clinical task. A pooled average alone can conceal failures on individual cases or subgroups.
| What to assess | Possible measure or analysis | When it matters |
|---|---|---|
| Overall overlap | Dice similarity or intersection over union | Summarizes shared area or volume, but may not reveal where a boundary error occurs or how consequential it is. |
| Boundary placement | Distance-based surface or boundary measure | Useful when the location of the contour boundary is clinically important. |
| Size or measurement | Volume or dimension error | Relevant when a measurement derived from the contour informs care. |
| Task-level failure | Missed structures or lesions; consequential under- or over-segmentation; downstream decision changes where applicable | Connects contour errors to the purpose the system is meant to serve. |
| Consistency and uncertainty | Confidence intervals and variation across cases, readers, sites, and relevant subgroups | Shows how stable performance is and where averages may be misleading. |
Examine distributions, outliers, and individual failure cases alongside aggregate results. A high mean overlap can coexist with a small number of severe failures, so report what went wrong and how those cases relate to the intended workflow.
Interpret Dice in the context of expert agreement
Dice is an overlap measure, not a standalone clinical acceptance decision. FDA’s SegAgree tool page states: “Traditional segmentation evaluation compares AI outputs against a reference standard aggregated from an expert panel using metrics such as Dice, but clinically meaningful cutoffs for these metrics are lacking, making objective performance targets difficult to define and borderline results hard to interpret.” The page was published 4 May 2026.
SegAgree offers one way to interpret overlap performance when a single reference standard or predefined cutoff is difficult to justify. It uses image-level pairwise device–expert and expert–expert Dice scores and reports the mean Dice difference with a 95% confidence interval. The comparison asks how device-to-expert dissimilarity relates to expert-to-expert dissimilarity; it does not establish a universal go/no-go threshold or prove clinical safety.
The tool’s stated scope is limited to overlap-based medical-image segmentation assessment. It does not address distance-based or other performance measures, and its method treats reader effect as fixed. FDA describes tool testing using statistical and image-based synthetic-contour simulations; that is not clinical testing of a segmentation product. See the FDA SegAgree page for its method and scope.
Test external validity and the real workflow
Once analytical performance has been evaluated on a locked system, assess whether it transfers to the intended clinical setting. Test data not used for training or tuning, including distinct sites or acquisition conditions relevant to deployment where feasible. Describe failures and their causes rather than presenting only a favorable aggregate.
Evaluate the complete human–AI workflow, not just the pixels:
- Can intended users recognize an implausible or incomplete contour?
- Can they correct it reliably, and does the interface make limitations or uncertainty visible?
- Does time pressure, workload, or integration with other systems change how carefully users review the output?
- Are missing inputs, system errors, and low-confidence or otherwise problematic outputs handled through a defined fallback or escalation path?
If the system is intended to support a clinical decision, examine whether using its output serves that purpose in the target population and care context. Pixel-level agreement alone cannot answer that question. FDA’s evaluation-methods page discusses performance assessment and uncertainty quantification; it describes research and methods, not a binding clinical validation protocol.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Determine applicable device obligations
Regulatory status and evidence requirements depend on the software function, claims, jurisdiction, and use context. FDA says software intended to acquire, process, or analyze a medical image may be a medical device; its examples include CT, X-ray, ultrasound, MRI, pathology, and dermatology images. This does not determine the status of every segmentation product. Review the relevant jurisdiction’s requirements for the specific function and claims rather than assuming that all image-segmentation software has the same obligations. FDA’s Step 6 software-function guidance is one U.S. starting point.
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For broader lifecycle context, WHO’s 2021 framework addresses evidence generation from development through post-market surveillance and is intended for developers, researchers, policymakers, and implementers. It is broad AI medical-device guidance, not a segmentation-specific standard. IMDRF’s final Good Machine Learning Practice guiding-principles document, dated 29 January 2025, provides international technical principles; applicable regulatory obligations still depend on jurisdiction and device function. Read the WHO framework and the IMDRF guiding principles in that context.
Plan monitoring and change control before deployment
Performance can change as scanners, protocols, patient populations, workflows, or model versions change. Define how the deployed system’s failures and performance will be reviewed, who is responsible, and what triggers investigation, rollback, retraining, or revalidation. The review cadence should be justified for the intended setting; the cited lifecycle frameworks do not prescribe one universal monitoring interval.
Govern changes to both the model and relevant data or workflow. Reassess performance when a change could affect intended behavior, and use post-market evidence to identify failures or distribution changes not captured before deployment. WHO’s framework includes post-market surveillance, while IMDRF’s AI/ML working group lists lifecycle management as ongoing work; see the IMDRF AI/ML working group.
Compare systems only on a like-for-like basis
If evaluating more than one system, compare them on the same intended task and data conditions. The following dimensions help identify whether an apparent performance difference is meaningful for the workflow:
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- Population, sites, modality, scanner, and protocol coverage.
- Anatomical and disease-case coverage, including relevant subgroups.
- Reference-reader expertise, annotation instructions, and adjudication design.
- Metric choice, uncertainty, consequential-case failures, and external-site performance.
- Human review and editing burden, workflow integration, and interoperability.
- Regulatory status and claims in the target jurisdiction, plus monitoring and change controls.
There is no universal metric threshold in the cited FDA material that ranks products or certifies readiness. Make the decision against the prespecified requirements for the particular use, including the consequences of an error and the safeguards available in that workflow.
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