  We present a fully convolutional neural network (ConvNet), named RatLesNetv2,
for segmenting lesions in rodent magnetic resonance (MR) brain images.
RatLesNetv2 architecture resembles an autoencoder and it incorporates residual
blocks that facilitate its optimization. RatLesNetv2 is trained end to end on
three-dimensional images and it requires no preprocessing. We evaluated
RatLesNetv2 on an exceptionally large dataset composed of 916 T2-weighted rat
brain MRI scans of 671 rats at nine different lesion stages that were used to
study focal cerebral ischemia for drug development. In addition, we compared
its performance with three other ConvNets specifically designed for medical
image segmentation. RatLesNetv2 obtained similar to higher Dice coefficient
values than the other ConvNets and it produced much more realistic and compact
segmentations with notably fewer holes and lower Hausdorff distance. The Dice
scores of RatLesNetv2 segmentations also exceeded inter-rater agreement of
manual segmentations. In conclusion, RatLesNetv2 could be used for automated
lesion segmentation, reducing human workload and improving reproducibility.
RatLesNetv2 is publicly available at https://github.com/jmlipman/RatLesNetv2.
