A Systematic Benchmark of Intensity Normalisation Methods for 3D Knee MRI Segmentation and Cross-Domain Generalisability
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TL;DR - This study benchmarks seven intensity-normalisation methods for 3D knee MRI meniscus segmentation. Normalisation modestly improves cross-domain robustness, but dataset shift remains the much larger deployment challenge.
- A 3D U-Net was trained on IWOAI 2019 and evaluated internally and externally on SKM-TEA.
- Normalisation methods performed similarly on internal data but differed significantly on external data.
- Z-score normalisation, Nyúl histogram matching, and CLAHE showed greater cross-domain robustness.
- All methods experienced a substantial performance drop between datasets, indicating that normalisation alone cannot overcome domain shift.
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A Systematic Benchmark of Intensity Normalisation Methods for 3D Knee MRI Segmentation and Cross-Domain Generalisability
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TL;DR - This study benchmarks seven intensity-normalisation methods for 3D knee MRI meniscus segmentation. Normalisation modestly improves cross-domain robustness, but dataset shift remains the much larger deployment challenge.
- A 3D U-Net was trained on IWOAI 2019 and evaluated internally and externally on SKM-TEA.
- Normalisation methods performed similarly on internal data but differed significantly on external data.
- Z-score normalisation, Nyúl histogram matching, and CLAHE showed greater cross-domain robustness.
- All methods experienced a substantial performance drop between datasets, indicating that normalisation alone cannot overcome domain shift.