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

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 two-year 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 re-training or modifications.