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A brain tumor segmentation model can produce different masks on scans from different hospitals because MRI images are shaped by the scanner, acquisition protocol, reconstruction and processing—not just by the anatomy being imaged. If the model learned from a narrow mix of sites, those differences can shift its inputs away from what it saw during training. Tumor biology and uncertainty in the reference masks add further variation, so a score change cannot automatically be blamed on the scanner alone.
Why MRI scans look different between scanners and sites
MRI intensity is not a fixed, directly comparable measurement in the way a calibrated ruler reading is. A scanner’s hardware and software, the sequence implementation, acquisition settings and image-processing pipeline all influence how tissue appears. Sites can differ in scanner vendor and generation, magnetic field strength, coil configuration, resolution, slice thickness, orientation, motion, and software version. Operators and local workflows matter too.
Even when two hospitals intend to use the same protocol, matching the names or nominal values of settings does not guarantee identical image formation. Vendors may implement sequences differently, and hardware can constrain what a scanner can actually acquire. These sources of non-biological variation are described in the 2025 ON-Harmony multi-site MRI resource study.
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The BraTS benchmark illustrates the range of acquisition conditions a model may encounter: its 2015 clinical data came from four centers, with scanners from different vendors, both 1.5 T and 3 T field strengths, and differing sequence implementations, including 2D and 3D acquisitions. This is an example of site diversity, not a claim that those settings represent every current hospital.
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How scanner and site differences affect a model
A segmentation model learns patterns from its training images. Those patterns can include tumor appearance, but they can also include correlations with scanner, site, protocol or processing. When deployed on a different scanner or at a new institution, the input distribution may change. A model can then respond differently even when the patient’s underlying anatomy and disease are otherwise comparable. This is commonly called domain shift.
The risk is not limited to moving between hospitals. A site can change over time after a scanner upgrade, software update, protocol revision, workflow change or shift in patient population. A 2025 review of deep learning for brain tumor MRI warns that models may fail to generalize beyond the sites and scanners represented during development, and that within-site changes can also degrade performance.
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Evidence from other MRI tasks helps explain why scanner sensitivity is plausible, but should not be mistaken for a brain tumor segmentation effect estimate. A 2023 structural-MRI study reported a drastic accuracy decline when disease-classification models trained on one manufacturer’s scans were tested on another manufacturer’s scans. It studied classification rather than tumor segmentation, so it does not establish how much a tumor segmentation score will fall.
Tumor appearance and reference masks also vary
Scanner shift is only one part of the explanation. Tumors differ in size, location, extent and tissue characteristics; treatment can leave cavities that displace normal structures. In addition, tumor boundaries are not always sharply visible. The BraTS authors note that lesion regions are defined by signal changes relative to surrounding normal tissue, and that smooth intensity gradients, partial-volume effects or bias-field artifacts can make borders difficult to identify even for experts.
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That uncertainty matters because evaluation compares a model’s mask with a reference mask. In the 2015 BraTS benchmark, expert raters’ masks had Dice overlap values of 74%–85% across tumor subregions. This is a benchmark-specific finding, not a universal expected level of agreement for all raters, datasets or annotation protocols.
Why one segmentation score can hide important differences
Overlap scores summarize agreement between two masks, but a single number does not describe every clinically relevant boundary error. Metrics respond differently to different kinds of mismatch, and the way scores are aggregated can change the apparent comparison between systems. A pooled score can also obscure whether a model performs unevenly across sites, scanners or tumor subregions.
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The BraTS authors found that different algorithms performed best on different tumor subregion subtasks; no single tested method ranked in the top five across all three. They also reported that metric choice could change rankings. A strong benchmark result therefore does not, by itself, establish that the same model will perform well at a new hospital.
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- Define the intended use and test population. Specify the tumor type, treatment status, tumor subregions, patient population and clinical workflow in which the model will be used. The test set should reflect that use rather than only the easiest or most common cases.
- Hold out sites or scanners. Keep one or more institutions or scanner groups entirely outside model development for external evaluation. A random split of scans from the same sites can measure performance on familiar domains without testing transfer to a genuinely new one.
- Record the acquisition domain. For each site, document scanner vendor and model, field strength, coils, software version, sequence and relevant acquisition settings, as well as processing steps. This makes it possible to interpret site-specific differences rather than treating all scans as interchangeable.
- Specify how reference masks were made. Describe the subregion definitions, annotation protocol, number and qualifications of raters, and any consensus process. Where masks vary, report that uncertainty instead of treating one reference as an unquestionable boundary.
- Report more than a pooled overlap score. Present results by site or scanner and by tumor subregion, alongside appropriate overlap and boundary-sensitive measures. State how metrics are aggregated so readers can see what the headline value represents.
- Check performance after changes. Reassess the model when a site changes scanner hardware, software, protocol or workflow, and monitor for shifts in the patient population. Validation at launch does not guarantee performance remains stable indefinitely.
What standardization and harmonization can—and cannot—do
Standardize acquisition where practical
Align sequence, resolution, orientation and protocol settings across sites when feasible, and keep an inventory of scanner and software changes. Standardization can reduce avoidable differences, but matching protocol settings does not ensure that different vendors or hardware produce identical images.
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Evaluate harmonization for the specific task
Methods such as ComBat can reduce scanner-associated variation in some settings, but they are not a universal correction for image differences or segmentation errors. A 2017 cortical-thickness study spanning 11 scanners found that ComBat reduced unwanted variability while improving statistical power and reproducibility for those measurements. By contrast, a 2023 structural-MRI classification study found no discernible classification benefit from its ComBat-based image strategy. Neither result establishes that harmonization will improve every brain tumor segmentation pipeline; evaluate it using the intended data and task.
Use repeated scans to separate scanner effects from biological differences
When feasible, scanning the same participants on multiple scanners helps distinguish between-scanner effects from differences in anatomy or disease across patient groups. ON-Harmony is one example of this kind of resource: its 2025 study describes 20 healthy participants scanned on six 3 T scanners from three major vendors at five sites, with repeat scans for some participants. It is a healthy-volunteer harmonization resource, not a tumor dataset or evidence of tumor segmentation performance.
How to compare reported results from different studies
Before comparing two systems, check whether they were evaluated under comparable conditions. A difference in headline scores may reflect a different test domain, tumor mix, annotation procedure or metric—not necessarily a better model.
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- Scanner and protocol coverage: Which vendors, field strengths, sequences and acquisition conditions were represented?
- Clinical and tumor mix: Were tumor type, treatment status and subregions comparable?
- Reference masks: How were labels created, and was rater disagreement or consensus handling reported?
- Metrics and aggregation: Which overlap and boundary measures were used, and were scores averaged per patient, subregion or site?
- Temporal testing: Was performance checked after scanner, software, protocol or workflow changes?
There is no general numerical estimate established here for how much brain tumor segmentation performance declines specifically because of scanner or site shift after accounting for tumor composition, protocols and label differences. Treat cross-task scanner studies and benchmark results as evidence about risks and evaluation design, not as a guaranteed loss or a forecast for a particular hospital.
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