  Delineating infarcted tissue in ischemic stroke lesions is crucial to
determine the extend of damage and optimal treatment for this life-threatening
condition. However, this problem remains challenging due to high variability of
ischemic strokes' location and shape. Recently, fully-convolutional neural
networks (CNN), in particular those based on U-Net, have led to improved
performances for this task. In this work, we propose a novel architecture that
improves standard U-Net based methods in three important ways. First, instead
of combining the available image modalities at the input, each of them is
processed in a different path to better exploit their unique information.
Moreover, the network is densely-connected (i.e., each layer is connected to
all following layers), both within each path and across different paths,
similar to HyperDenseNet. This gives our model the freedom to learn the scale
at which modalities should be processed and combined. Finally, inspired by the
Inception architecture, we improve standard U-Net modules by extending
inception modules with two convolutional blocks with dilated convolutions of
different scale. This helps handling the variability in lesion sizes. We split
the 93 stroke datasets into training and validation sets containing 83 and 9
examples respectively. Our network was trained on a NVidia TITAN XP GPU with 16
GBs RAM, using ADAM as optimizer and a learning rate of 1$\times$10$^{-5}$
during 200 epochs. Training took around 5 hours and segmentation of a whole
volume took between 0.2 and 2 seconds, as average. The performance on the test
set obtained by our method is compared to several baselines, to demonstrate the
effectiveness of our architecture, and to a state-of-art architecture that
employs factorized dilated convolutions, i.e., ERFNet.
