Technical and clinical validation of a novel deep learning-based white matter hyperintensity segmentation tool

Gesierich, B.; Pirpamer, L.; Meier, D.S.; Amann, M.; Cerfontaine, M.N.; Leeuw, F.E. de; Maillard, P.; Moy, S.; Helmer, K.G.; Kühne, M.; Bonati, L.H.; Rutten, J.W.; Oberstein, S.A.J.L.; Duering, M.; Alzheimers Dis Neuroimaging Initiative

doi:doi:10.1016/j.cccb.2025.100393

Introduction: White matter hyperintensities (WMH) on MRI are a hallmark of cerebral small vessel disease. Although numerous WMH segmentation tools exist, each presents relevant limitations that can impact their usability. This research aimed to develop, validate, and disseminate a novel WMH segmentation algorithm to address these limitations. Methods: Using an intentionally heterogeneous dataset, we trained models based on the MD-GRU and nnU-Net deep learning algorithms. The new models were benchmarked in both technical and clinical validation against current state-of-the-art algorithms, utilizing datasets that were not included in the training data. For technical validation in patients, we assessed bias and precision against reference masks, scan-rescan repeatability and inter-scanner reproducibility in data from the MarkVCID consortium. Segmentation performance on 2D data was evaluated using the SWISS-AF dataset. For clinical validation, we determined percent volume change over a...Show moreIntroduction: White matter hyperintensities (WMH) on MRI are a hallmark of cerebral small vessel disease. Although numerous WMH segmentation tools exist, each presents relevant limitations that can impact their usability. This research aimed to develop, validate, and disseminate a novel WMH segmentation algorithm to address these limitations. Methods: Using an intentionally heterogeneous dataset, we trained models based on the MD-GRU and nnU-Net deep learning algorithms. The new models were benchmarked in both technical and clinical validation against current state-of-the-art algorithms, utilizing datasets that were not included in the training data. For technical validation in patients, we assessed bias and precision against reference masks, scan-rescan repeatability and inter-scanner reproducibility in data from the MarkVCID consortium. Segmentation performance on 2D data was evaluated using the SWISS-AF dataset. For clinical validation, we determined percent volume change over a twoyear follow-up in the DiViNAS study and calculated statistical power to detect treatment effects. Results: The newly trained algorithms outperformed the benchmarking algorithms, demonstrating better agreement with reference volumes, as well as less bias and higher precision in the repeatability and reproducibility experiments. The nnU-Net algorithm exhibited the highest statistical power for detecting treatment effects, requiring a 41 % smaller sample size than the best-performing benchmarking algorithm. Conclusion: We developed and systematically validated two novel WMH segmentation algorithms, which demonstrated excellent generalization capabilities. The comprehensive, user-friendly processing pipelines are publicly available as prebuilt software containers and can be applied to a wide range of datasets without retraining or modifications.Show less

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Technical and clinical validation of a novel deep learning-based white matter hyperintensity segmentation tool