Automatic hemisphere segmentation in rodent MRI with lesions

We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment brain hemispheres in magnetic resonance (MR) images of rodents 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. Using a large dataset of 723 MR rat brain images, we evaluated our MedicDeepLabv3+, two state-of-the-art convolutional neural networks (DeepLabv3+, UNet) and three approaches that were specifically designed for skull-stripping rodent MR images (Demon, RATS and RBET). 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 to only three images, 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 rodent neuroimaging studies.