Using Deep Convolutional Neural Network for Mouse Brain Segmentation in DT-MRI

Mice are routinely used as an animal model for brain research. Therefore, automatic and robust mouse brain segmentation is an essential task in many applications since it affects the outcomes of the entire analysis. Automated human brain segmentation have been well studied. However, applying existing methods for human brain segmentation directly to the mouse brain is not immediately applicable due to the difference in size, shape and structure between the human and mouse brains. In this paper, we present an automatic mouse brain segmentation method based on a deep convolutional neural network (CNN) called U-Net. Quantitative assessment of the proposed method is performed on 26 mouse brain diffusion MRI studies with a reference standard obtained from expert manual segmentation. We also compared the segmentation result with several state-of-art human brain segmentation methods. The result shows that the proposed CNN model outperforms other methods yielding an average Dice coefficient of 0.974, Hausdorff distance of 3.875 mm, and mean surface distance of 0.134 mm.

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