Ms. Cassandra Meyer Profile

Cassandra Meyer

at UCLA Brain Mapping Ctr

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Proceedings Article | 4 April 2022 Presentation + Paper

Mouse brain extraction using 2-Stage CNNs

Yeun Kim, Evelyn Malamut, Cassandra Meyer, Allan MacKenzie-Graham, David Shattuck

Proceedings Volume 12032, 120321F (2022) https://doi.org/10.1117/12.2609004

KEYWORDS: Brain, Magnetic resonance imaging, Neuroimaging, Image segmentation, 3D image processing, Image processing, Neural networks

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Magnetic resonance imaging of mice and other small animals plays an important role in translational medicine, where animal models are essential in the study of diseases and their potential treatments. While a large number of brain imaging studies using mice are conducted every year, there are few tools designed specifically for analyzing mouse MRI. Researchers often resort to adapting tools designed for processing MRI of human brains to work for the different structure, composition, and appearance of the mouse brain. While these methods may provide a reasonable initialization, researchers often have to perform extensive manual editing. In this work, we adapted a patch-based 2-Stage CNN Architecture to segment brain and non-brain in mouse MRI. We trained our model using brain MRI of healthy mice and mice with experimental autoimmune encephalomyelitis. These images had been previously acquired by our research team and processed to extract the brain using detailed manual editing. We compared our method with five existing tools, including BSE,¹ rBET,² AFNI,³ PCNN,⁴ and nnU-Net,⁵ using manually delineated mouse MRI. Both our method and nnU-Net achieved mean Dice scores on the order of 0.99 and HD95 measures on the order of one voxel, substantially outperforming the other methods. Our proposed method had slightly better Dice, HD99, and sensitivity measures than nnU-Net, while nnU-Net had slightly better HD95 scores. While these differences were small enough that further evaluation of the methods on a broader set of images would be warranted, they do suggest that our proposed method is competitive with a state-of-the-art deep learning method.

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