Development and validation of a two-stage convolutional neural network algorithm for segmentation of MRI white matter hyperintensities for longitudinal studies in CADASIL.

Background: Segmentation of white matter hyperintensities (WMH) in CADASIL, one of the most severe cerebral small vessel disease of genetic origin, is challenging.

Method: We adapted and validated an automatic method based on a convolutional neural network (CNN) algorithm and using a large dataset of 2D and/or 3D FLAIR and T1-weighted images acquired in 132 patients, to measure the progression of WMH in this condition.

Results: The volume of WMH measured using this method correlated strongly with reference data validated by experts. WMH segmentation was also clearly improved compared to the BIANCA segmentation method. Combining two successive learning models was found to be of particular interest, reducing the number of false-positive voxels and the extent of under-segmentation detected after a single-stage process. With the two-stage approach, WMH progression correlated with measures derived from the reference masks for lesions increasing with age, and with the variable WMH progression trajectories at individual level. We also confirmed the expected effect of the initial load of WMH and the influence of the type of MRI acquisition on measures of this progression.

Conclusion: Altogether, our findings suggest that WMH progression in CADASIL can be measured automatically with adequate confidence by a CNN segmentation algorithm.