Evaluate an AI medical image segmentation system against the job it is meant to do—not against a universal Dice-score cutoff. Define what counts as the reference standard, choose complementary metrics for the errors that matter, keep test patients separate from training data, and report how performance changes across sites, acquisition protocols, subgroups, and reasonable analysis choices.
Start with the intended use
A segmentation is a mask or set of masks that identifies structures or findings in an image. The same mask can serve very different purposes: measuring a tumor, planning a procedure, supporting triage, or enabling research. An error that is tolerable for exploratory measurement may be unacceptable if it changes a treatment plan. The use case determines what to evaluate.
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Before selecting metrics, specify:
- Target and population: the anatomy or pathology, relevant patient characteristics, and care setting.
- Inputs: modality, sequences or protocols, and any required combination of images.
- Output: classes or structures segmented, and whether results are shown as contours, used for a measurement, or passed into another system.
- Decision and unit of analysis: whether the segmentation informs measurement, planning, treatment, triage, or research—and whether success is judged per voxel, lesion, image, patient, or downstream decision.
This framing prevents a common mismatch: reporting a strong average overlap score when the practical question is whether small lesions are missed, boundaries are accurate enough for planning, or the result changes a clinical decision. The FDA notes that distinct intended applications require distinct performance metrics.
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Define the reference standard before scoring
Expert annotations are not automatically an unquestionable “ground truth.” Readers may disagree about where a boundary lies, whether an ambiguous finding belongs in a class, or which annotation convention to follow. State how the reference labels were created so a reader can judge what a model-to-label difference means.
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- Identify annotators’ relevant expertise and the annotation instructions, tools, and workflow.
- Say whether the reference comes from one reader, a consensus, adjudication, pathology, or another source.
- Explain how disagreements and uncertain regions were handled, including any excluded cases or voxels.
- Report inter-reader variability—and intra-reader variability when available—using measures relevant to the task.
Use “reference standard” where appropriate, rather than implying that a label is perfectly certain. If multiple experts are available, their variation gives context for interpreting model disagreement; it does not by itself show that a model is clinically interchangeable with a reader.
How FDA’s SegAgree fits
The FDA’s SegAgree tool compares overlap-based device-to-expert variability with expert-to-expert variability. It uses image-level pairwise Dice scores and returns a mean Dice difference with a 95% confidence interval. The FDA catalog entry was published May 4, 2026. SegAgree is intended to help interpret borderline traditional overlap results, not to establish a universal acceptance threshold. It does not assess distance-based performance, so it cannot replace boundary analysis or evaluation of downstream clinical utility.
Choose metrics for the errors that matter
No single metric describes every kind of segmentation error. Use a small, justified set of complementary measures and explain why each matches the intended use. A benchmark’s usual metric is not, by itself, a reason to select it.
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|---|---|---|
| Dice similarity coefficient | How much the predicted region overlaps the reference. | Useful for overlap, but a single score can hide contour displacement, missed small targets, or failures in a subgroup. It has no universal clinical cutoff. |
| Jaccard index (IoU) | Overlap as a fraction of the combined area or volume of prediction and reference. | Like Dice, it summarizes overlap rather than the clinical importance or location of an error. |
| Sensitivity and precision | Sensitivity reflects detected reference-positive voxels or targets; precision reflects how much of the prediction is supported by the reference. | Useful for distinguishing missed targets from over-segmentation. State whether results are voxel- or lesion-level. |
| Specificity | How often reference-negative units are correctly identified as negative. | For voxel-level segmentation, a large background can dominate the result; report it only with that context. |
| Hausdorff or other boundary-distance measures | How far predicted and reference boundaries are apart. | Can reveal contour errors that overlap averages obscure. Specify the distance definition and report distances in physical units when appropriate. |
| Lesion-level results | Whether lesions are found, missed, or split/merged under the study’s matching rules. | Especially important for small lesions and rare classes, where pooled voxel scores may conceal clinically relevant failures. |
Other measures—including Rand index, ROC curves, and Cohen’s kappa—appear in the medical image segmentation metrics literature. Their usefulness depends on the task and implementation; adding a metric without explaining its interpretation does not make an evaluation more complete. Müller, Soto-Rey, and Kramer’s 2022 review cautions that assessment can be unreliable when metrics are implemented or used incorrectly.
Make metric calculations reproducible
For each reported measure, specify whether it is averaged per case, per class, or over all voxels; whether class averages are macro- or micro-averaged; how empty reference or prediction masks are handled; and what thresholding and postprocessing were applied. For distance measures, report the image spacing and whether distances use physical coordinates. State how the results handle multiple lesions and rare classes. These details can change the apparent result, especially when cases vary greatly in size.
Separate internal testing from external testing
Test data must be independent at the patient level or higher: images from one patient should not leak across training and test partitions. Describe how cases were assigned and distinguish internal testing on held-out data from the development source from external testing on a genuinely external dataset, such as one from another institution. CLAIM 2024 recommends these terms in preference to the ambiguous label “validation.”
Report inclusion and exclusion criteria, dates, demographics, relevant clinical characteristics, class imbalance, and how each test dataset relates to the intended-use population. Where relevant, examine performance across institutions, scanners, vendors, protocols, and clinically meaningful population subgroups. A pooled result can look reassuring even when a system performs poorly in a smaller site or group.
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|---|---|
| Partition independence | Patient-level or stronger separation; explain assignment and any relationships between datasets. |
| Internal testing | Held-out cases from the development source, with cohort and selection details. |
| External testing | A fully external dataset, with site and acquisition differences described. |
| Subgroups and classes | Results for relevant demographic or clinical groups, small structures, and rare classes rather than pooled metrics alone. |
Report acquisition conditions for every modality
“CT,” “MRI,” or “ultrasound” alone is not enough to reproduce a segmentation study or judge whether its test images resemble the intended deployment setting. CLAIM 2024 calls for acquisition details, including parameters that differ by modality. Report the manufacturer and the parameters material to the task, along with preprocessing and resampling.
| Modality or study setup | Acquisition and processing details to report |
|---|---|
| MRI | Sequence and relevant protocol details, spatial resolution, slice thickness, scan range, and preprocessing or resampling. |
| Ultrasound | Transducer frequency and relevant acquisition conditions, image resolution, scan range, and preprocessing. |
| CT | Energy and current, slice thickness, scan range, resolution, and preprocessing or resampling. |
| Multimodal input | All component modalities and their availability in intended use; registration and alignment methods; handling of missing inputs; and how images are fused. |
These are reporting prompts, not a claim that each parameter affects every task equally. Explain which acquisition differences matter for the target and how the test set represents them.
Quantify uncertainty and probe robustness
A point estimate does not show how precise a result is or whether it depends on a narrow set of choices. Report confidence intervals or another suitable uncertainty estimate, the statistical method used, and paired comparisons on the same cases when comparing systems. Describe the source of uncertainty where possible: limited data, uncertain labels, or variation between readers, sites, and acquisitions.
Test sensitivity to reasonable changes in preprocessing, thresholds, postprocessing, acquisition conditions, site, and reference annotations. Report clinically relevant subgroup performance and identify the weakest settings instead of letting an overall average stand in for every patient or scanner. CLAIM 2024 recommends reporting uncertainty and sensitivity or robustness analyses; the FDA’s evaluation work also highlights label uncertainty and other uncertainty sources.
Use a decision-oriented comparison
When comparing two segmentation systems—or deciding whether a reported evaluation is persuasive—review the whole evidence package rather than ranking systems by one score.
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| Comparison axis | Questions to ask |
|---|---|
| Intended use | What decision does the output support, and what is the consequence of a miss, false positive, or boundary error? |
| Reference quality | Who annotated the cases, how were disagreements resolved, and what reader variability was measured? |
| Spatial and target-level performance | Are overlap, boundary, and lesion-level errors reported where relevant? |
| Generalization | Are test patients independent? Is there genuinely external testing across sites and acquisition protocols? |
| Class and subgroup behavior | Are small structures, rare classes, and clinically relevant groups visible in the results? |
| Precision and robustness | Are uncertainty intervals and sensitivity analyses provided, and are weak conditions identified? |
| Reproducibility | Can a reader understand acquisition, preprocessing, partitioning, metric implementation, and postprocessing? |
CLAIM 2024 is a reporting checklist for medical imaging AI studies, not a universal performance standard. Its update process involved 72 panel members who completed two rounds in 2024. Use it to check whether a study reports enough to interpret and reproduce its evaluation—not as proof that a system is clinically effective.
What counts as a convincing evaluation?
A convincing evaluation connects intended use to a transparent reference standard, task-appropriate complementary metrics, patient-independent testing, and acquisition conditions representative of use. It also shows uncertainty, subgroup behavior, and robustness rather than presenting one pooled overlap score as a complete verdict. The specific metric suite and acceptable performance remain task-dependent; the sources here establish no modality-independent Dice threshold for “good segmentation.”
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