Abstract
White Matter Hyperintensities (WMH) are imaging biomarkers which indicate cerebral microangiopathy, a risk factor for stroke and vascular dementia. When training Deep Neural Networks (DNN) to segment WMH, data pooling may be used to increase the training dataset size. However, it is not yet fully understood how pooling of heterogeneous data influences the segmentation performance. In this contribution, we investigate the impact of sampling ratios between different datasets with varying data quality and lesion volumes. We observe systematic changes in DNN performance and segmented lesion volume depending on the sampling ratio. If properly chosen, a single DNN can accurately segment and quantify both large and small lesions on different quality test data without loss of performance compared with a specialized DNN.
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Acknowledgements
The authors received funding from the German Federal Ministry for Economic Affairs and Energy (Grant No. ZF4173705CR7 and No. ZF4434802CR7) and from the European Union Seventh Framework Program (Grant No. 278276). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
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Hänsch, A. et al. (2019). Data Pooling and Sampling of Heterogeneous Image Data for White Matter Hyperintensity Segmentation. In: Zhou, L., et al. OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging. OR 2.0 MLCN 2019 2019. Lecture Notes in Computer Science(), vol 11796. Springer, Cham. https://doi.org/10.1007/978-3-030-32695-1_10
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