RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation

Segmentation of rodent brain lesions on magnetic resonance images (MRIs) is a time-consuming task with high inter- and intra-operator variability due to its subjective nature. We present a three-dimensional fully convolutional neural network (ConvNet) named RatLesNetv2 for segmenting rodent brain lesions. We compare its performance with other ConvNets on an unusually large and heterogeneous data set composed by 916 T2-weighted rat brain scans at nine different lesion stages. RatLesNetv2 obtained similar to higher Dice coefficients than the other ConvNets and it produced much more realistic and compact segmentations with notably less holes and lower Hausdorff distance. RatLesNetv2-derived segmentations also exceeded inter-rater agreement Dice coefficients. Additionally, we show that training on disparate ground truths leads to significantly different segmentations, and we study RatLesNetv2 generalization capability when optimizing for training sets of different sizes. RatLesNetv2 is publicly available at https://github.com/jmlipman/RatLesNetv2.