  Rodent stroke models are important for evaluating treatments and
understanding the pathophysiology and behavioral changes of brain ischemia, and
magnetic resonance imaging (MRI) is a valuable tool for measuring outcome in
preclinical studies. Brain extraction is an essential first step in most
neuroimaging pipelines; however, it can be challenging in the presence of
severe pathology and when dataset quality is highly variable. Convolutional
neural networks (CNNs) can improve accuracy and reduce operator time,
facilitating high throughput preclinical studies. As part of an ongoing
preclinical stroke imaging study, we developed a deep-learning mouse brain
extraction tool by using a U-net CNN. While previous studies have evaluated
U-net architectures, we sought to evaluate their practical performance across
data types. We ask how performance is affected with data across: six imaging
centers, two time points after experimental stroke, and across four MRI
contrasts. We trained, validated, and tested a typical U-net model on 240
multimodal MRI datasets including quantitative multi-echo T2 and apparent
diffusivity coefficient (ADC) maps, and performed qualitative evaluation with a
large preclinical stroke database (N=1,368). We describe the design and
development of this system, and report our findings linking data
characteristics to segmentation performance. We consistently found high
accuracy and ability of the U-net architecture to generalize performance in a
range of 95-97% accuracy, with only modest reductions in performance based on
lower fidelity imaging hardware and brain pathology. This work can help inform
the design of future preclinical rodent imaging studies and improve their
scalability and reliability.
