Abstract
White matter hyperintensities (WHM) are characteristics of various brain diseases, so automated detection tools have a broad clinical spectrum. Deep learning architectures have been recently very successful for the segmentation of brain lesions, such as ictus or tumour lesions. We propose a Convolutional Neural Network composed of four parallel data paths whose input is a mixture of 2D/3D windows extracted from multimodal magnetic resonance imaging of the brain. The architecture is lighter than others proposed in the literature for lesion detection so its training is faster. We carry out computational experiments on a dataset of multimodal imaging from 18 subjects, achieving competitive results with state of the art approaches.
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Roa-Barco, L. et al. (2018). A 2D/3D Convolutional Neural Network for Brain White Matter Lesion Detection in Multimodal MRI. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_39
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