  We present MedicDeepLabv3+, a convolutional neural network that is the first
completely automatic method to segment cerebral hemispheres in magnetic
resonance (MR) volumes of rats with lesions. MedicDeepLabv3+ improves the
state-of-the-art DeepLabv3+ with an advanced decoder, incorporating spatial
attention layers and additional skip connections that, as we show in our
experiments, lead to more precise segmentations. MedicDeepLabv3+ requires no MR
image preprocessing, such as bias-field correction or registration to a
template, produces segmentations in less than a second, and its GPU memory
requirements can be adjusted based on the available resources. We optimized
MedicDeepLabv3+ and six other state-of-the-art convolutional neural networks
(DeepLabv3+, UNet, HighRes3DNet, V-Net, VoxResNet, Demon) on a heterogeneous
training set comprised by MR volumes from 11 cohorts acquired at different
lesion stages. Then, we evaluated the trained models and two approaches
specifically designed for rodent MRI skull stripping (RATS and RBET) on a large
dataset of 655 MR rat brain volumes. In our experiments, MedicDeepLabv3+
outperformed the other methods, yielding an average Dice coefficient of 0.952
and 0.944 in the brain and contralateral hemisphere regions. Additionally, we
show that despite limiting the GPU memory and the training data, our
MedicDeepLabv3+ also provided satisfactory segmentations. In conclusion, our
method, publicly available at https://github.com/jmlipman/MedicDeepLabv3Plus,
yielded excellent results in multiple scenarios, demonstrating its capability
to reduce human workload in rat neuroimaging studies.
