Table_1_Automated Skull Stripping in Mouse Functional Magnetic Resonance Imaging Analysis Using 3D U-Net.DOCX (74.94 kB)
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Table_1_Automated Skull Stripping in Mouse Functional Magnetic Resonance Imaging Analysis Using 3D U-Net.DOCX

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posted on 10.03.2022, 14:20 authored by Guohui Ruan, Jiaming Liu, Ziqi An, Kaiibin Wu, Chuanjun Tong, Qiang Liu, Ping Liang, Zhifeng Liang, Wufan Chen, Xinyuan Zhang, Yanqiu Feng

Skull stripping is an initial and critical step in the pipeline of mouse fMRI analysis. Manual labeling of the brain usually suffers from intra- and inter-rater variability and is highly time-consuming. Hence, an automatic and efficient skull-stripping method is in high demand for mouse fMRI studies. In this study, we investigated a 3D U-Net based method for automatic brain extraction in mouse fMRI studies. Two U-Net models were separately trained on T2-weighted anatomical images and T2*-weighted functional images. The trained models were tested on both interior and exterior datasets. The 3D U-Net models yielded a higher accuracy in brain extraction from both T2-weighted images (Dice > 0.984, Jaccard index > 0.968 and Hausdorff distance < 7.7) and T2*-weighted images (Dice > 0.964, Jaccard index > 0.931 and Hausdorff distance < 3.3), compared with the two widely used mouse skull-stripping methods (RATS and SHERM). The resting-state fMRI results using automatic segmentation with the 3D U-Net models are highly consistent with those obtained by manual segmentation for both the seed-based and group independent component analysis. These results demonstrate that the 3D U-Net based method can replace manual brain extraction in mouse fMRI analysis.

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