  Radiologists use various imaging modalities to aid in different tasks like
diagnosis of disease, lesion visualization, surgical planning and prognostic
evaluation. Most of these tasks rely on the the accurate delineation of the
anatomical morphology of the organ, lesion or tumor. Deep learning frameworks
can be designed to facilitate automated delineation of the region of interest
in such cases with high accuracy. Performance of such automated frameworks for
medical image segmentation can be improved with efficient integration of
information from multiple modalities aided by suitable learning strategies. In
this direction, we show the effectiveness of residual network trained
adversarially in addition to a boundary weighted loss. The proposed methodology
is experimentally verified on the SPES-ISLES 2015 dataset for ischaemic stroke
segmentation with an average Dice coefficient of $0.881$ for penumbra and
$0.877$ for core. It was observed that addition of residual connections and
boundary weighted loss improved the performance significantly.
