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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Akkus, Z., et al.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30(4), 449–459 (2017). https://doi.org/10.1007/s10278-017-9983-4
Ashton, E.A., et al.: Accuracy and reproducibility of manual and semiautomated quantification of MS lesions by MRI. J. Magn. Reson. Imaging 17(3), 300–308 (2003). https://doi.org/10.1002/jmri.10258
Debette, S., Markus, H.S.: The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 341, c3666 (2010). https://doi.org/10.1136/bmj.c3666
Kuijf, H.J., et al.: Standardized assessment of automatic segmentation of white matter hyperintensities; results of the WMH segmentation challenge. IEEE Trans. Med. Imaging (2019). https://doi.org/10.1109/TMI.2019.2905770
Li, H., et al.: Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images. NeuroImage 183, 650–665 (2018). https://doi.org/10.1016/j.neuroimage.2018.07.005
Milletari, F., et al.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 4th International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016). https://doi.org/10.1109/3DV.2016.79
Moeskops, P., et al.: Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI. NeuroImage: Clin. 17, 251–262 (2018). https://doi.org/10.1016/j.nicl.2017.10.007
Pantoni, L.: Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol. 9(7), 689–701 (2010). https://doi.org/10.1016/S1474-4422(10)70104-6
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Thomalla, G., et al.: MRI-guided thrombolysis for stroke with unknown time of onset. N. Engl. J. Med. 379(7), 611–622 (2018). https://doi.org/10.1056/NEJMoa1804355
Wardlaw, J.M., et al.: Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 12(8), 822–838 (2013). https://doi.org/10.1016/S1474-4422(13)70124-8
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-32695-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32694-4
Online ISBN: 978-3-030-32695-1
eBook Packages: Computer ScienceComputer Science (R0)
