White matter hyperintensities (WMH), hyperintense on T2-weighted FLAIR images are prominent features of demyelination and axonal degeneration in cerebral white matter. The time-consuming nature of manual segmentation necessitates the need for faster and reliable automated segmentation algorithms. In this work, we propose three deep learning architectures for WMH detection on 3D FLAIR images: a modified UNET3D, Res-UNET3D and their ensemble combination. Two UNET3D and two Res-UNET3D were trained with random initialization using 3D patches sampled from within the brain. The posterior probabilities for WMH from individual networks were averaged to obtain a revised posterior probability for the ensemble. Performance of the individual networks as well as that of the ensemble was assessed using dice and precision scores.
It was observed that the ensemble of 3D networks yields improved dice and precision scores in comparison to an average of individual networks, thereby reducing the effect of choice of network or parameters. Furthermore, the average dice scores for the ensemble approached the inter-observer variability of human observers.
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