  Manual segmentation of rodent brain lesions from magnetic resonance images
(MRIs) is an arduous, time-consuming and subjective task that is highly
important in pre-clinical research. Several automatic methods have been
developed for different human brain MRI segmentation, but little research has
targeted automatic rodent lesion segmentation. The existing tools for
performing automatic lesion segmentation in rodents are constrained by strict
assumptions about the data. Deep learning has been successfully used for
medical image segmentation. However, there has not been any deep learning
approach specifically designed for tackling rodent brain lesion segmentation.
In this work, we propose a novel Fully Convolutional Network (FCN), RatLesNet,
for the aforementioned task. Our dataset consists of 131 T2-weighted rat brain
scans from 4 different studies in which ischemic stroke was induced by
transient middle cerebral artery occlusion. We compare our method with two
other 3D FCNs originally developed for anatomical segmentation (VoxResNet and
3D-U-Net) with 5-fold cross-validation on a single study and a generalization
test, where the training was done on a single study and testing on three
remaining studies. The labels generated by our method were quantitatively and
qualitatively better than the predictions of the compared methods. The average
Dice coefficient achieved in the 5-fold cross-validation experiment with the
proposed approach was 0.88, between 3.7% and 38% higher than the compared
architectures. The presented architecture also outperformed the other FCNs at
generalizing on different studies, achieving the average Dice coefficient of
0.79.
