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A brain tumor segmentation model can produce different masks on scans of the same kind of disease because MRI images are shaped by the scanner, acquisition protocol, processing, patient and tumor characteristics, and the labels used to train and evaluate the model. A model may learn patterns tied to the hospitals and scanners in its training data as well as patterns that indicate tumor. When it meets a different imaging distribution, its performance can change. Uncertain tumor boundaries can compound that shift.
How MRI scanner and site differences change the input
MRI does not provide a single, fixed-intensity measurement of tissue. Image appearance depends on how a scan is acquired and reconstructed, so scans of comparable anatomy can differ in contrast, noise, artifacts, and spatial detail. Differences between sites can also reflect patient and tumor composition, not just scanner hardware. Unless a study accounts for those factors, a performance gap between hospitals cannot be attributed to the scanner alone.
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| Source of variation | How it can affect the image or comparison |
|---|---|
| Scanner hardware and software | Vendors and scanner generations may implement pulse sequences and reconstruction differently, changing image appearance. |
| Field strength and coils | Field strength, coil channels, and coil geometry can affect contrast, noise, and the spatial information available to a model. |
| Acquisition protocol | Sequence, resolution, slice thickness, orientation, and other sequence parameters affect contrast and detail. Nominally matching settings do not necessarily produce identical images across vendors. |
| Motion, operators, and processing | Patient motion, operator choices, and image-processing methods can introduce further differences between scans and sites. |
| Site population and workflow | Patient demographics, tumor characteristics, and changes in local workflow may affect the data being evaluated, independently of scanner effects. |
The 2015 BraTS benchmark illustrates how varied clinical MRI data can be: its scans came from four centers, used multiple scanner vendors and both 1.5 T and 3 T field strengths, and included different sequence implementations, including 2D and 3D acquisitions. It is a historical benchmark example, not a description of every current hospital or scanner.
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A model learns from the examples it sees. If particular scanners, protocols, or sites are overrepresented in training, the model may learn image patterns associated with those sources alongside the features that matter for tumor segmentation. A new hospital can present different contrasts, noise, artifacts, or spatial detail, shifting the model’s inputs even when the clinical task is unchanged.
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A 2025 review focused on brain tumor MRI deep learning warns that models may not generalize beyond the sites and scanners used during development. The problem can arise within a single institution, too: scanner software, protocols, workflow, or patient demographics can change over time. An earlier evaluation that passed does not establish that a model remains reliable after those conditions change.
A 2023 study found a drastic accuracy decline when structural-MRI disease-classification models trained on one manufacturer were tested on another. That supports the broader point that MRI models can be scanner-sensitive, but it is not an estimate of performance loss for brain tumor segmentation: the study examined a different task and outcome.
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Tumor boundaries and reference masks add uncertainty
Not every disagreement between a predicted mask and a reference mask is caused by scanner shift. Tumors vary in size, location, extent, and tissue characteristics; treatment-related cavities can also displace normal structures. In some regions, the boundary is intrinsically difficult to define from the image. The BraTS authors explain that tumor regions are identified through signal changes relative to surrounding normal tissue, and that partial-volume effects or bias-field artifacts can obscure borders.
Human annotations are therefore not a perfectly fixed ground truth. In the 2015 BraTS benchmark, expert inter-rater Dice scores ranged from 74% to 85% across tumor subregions. This is a benchmark-specific result, not a universal estimate of how much experts disagree in other annotation settings.
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Evaluation choices matter as well. BraTS found that different algorithms performed best on different tumor subregion tasks, and no single tested method ranked in the top five across all three subtasks. Rankings could change with the metric because overlap scores and other measures respond differently to error types. A single pooled score can hide where a mask is inaccurate or which tumor region is affected.
What to do when validating a model at a new site
- Define the intended deployment. Specify the hospitals, scanner types, patient groups, tumor types, treatment status, and tumor subregions the model is meant to handle. These define the conditions an evaluation needs to represent.
- Hold out sites or scanners for external testing. Reserve one or more institutions or scanners that were not used for training or model selection. A random split that places scans from the same sites in both training and testing does not establish performance at an unseen hospital.
- Represent the deployment population. Include the scanner and protocol diversity, patient mix, and relevant tumor cases expected in actual use. Report which groups are represented so readers can judge what the test does and does not cover.
- Document how the reference masks were made. State the tumor-region definitions, annotation protocol, rater process, and how disagreements or consensus were handled. Where labels are uncertain, make that uncertainty visible rather than treating every boundary as exact.
- Report more than one summary score. Show subregion and per-site or per-scanner results, alongside suitable overlap and boundary-sensitive measures. Describe metric definitions and aggregation, since different metrics and tumor subregions can produce different comparisons.
- Check performance over time. Re-evaluate after scanner upgrades, software changes, protocol revisions, or workflow changes. A site can move away from the conditions represented in the original evaluation without changing its name or location.
Can harmonization fix scanner differences?
Harmonization can reduce some scanner-associated variation, but it is not a universal correction for every image, feature, or model. Standardizing acquisition prospectively can help: align sequence, resolution, orientation, and protocol settings as far as practical, and record vendor, scanner model, field strength, coils, software, and upgrades. Matching parameter names alone does not ensure the same image formation across equipment.
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Results from other MRI applications show why harmonization should be evaluated for the task rather than assumed to work. In a cortical-thickness study spanning 11 scanners, Fortin et al. (2017) found that ComBat reduced unwanted variability while improving statistical power and reproducibility. A separate structural-MRI classification study found no discernible classification benefit from its ComBat-based image strategy. Neither result establishes that ComBat, or another harmonization method, will improve a brain tumor segmentation pipeline.
Travelling-heads and scan-rescan designs can help separate scanner effects from biological differences by scanning the same people on multiple systems. The 2025 ON-Harmony resource includes 20 healthy participants scanned on six 3 T scanners from three vendors at five sites, with repeat scans for some participants. It is a harmonization resource, not a tumor dataset or evidence of segmentation performance.
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How to judge claims about model performance
When comparing segmentation systems or studies, look beyond the headline score. Check whether the test data are independent by site or scanner; which vendors, field strengths, sequences, and protocols are represented; which tumor types, treatment states, and subregions were evaluated; how reference labels were produced; which metrics and aggregation methods were used; and whether performance was checked after equipment or protocol changes.
There is no general numerical estimate here for the amount of brain tumor segmentation performance loss caused specifically by scanner or site shift after controlling for tumor mix, labels, and protocols. The available findings establish reasons to expect variability and ways to test for it, but they do not provide a universal loss figure or guarantee for an individual model.
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