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Segmenting vascular pathologies such as white matter lesions in Brainmagnetic resonance images (MRIs) require acquisition of multiple sequences suchas T1-weighted (T1-w) --on which lesions appear hypointense-- and fluidattenuated inversion recovery (FLAIR) sequence --where lesions appearhyperintense--. However, most of the existing retrospective datasets do notconsist of FLAIR sequences. Existing missing modality imputation methodsseparate the process of imputation, and the process of segmentation. In thispaper, we propose a method to link both modality imputation and segmentationusing convolutional neural networks. We show that by jointly optimizing theimputation network and the segmentation network, the method not only producesmore realistic synthetic FLAIR images from T1-w images, but also improves thesegmentation of WMH from T1-w images only.

Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs