TY - JOUR T1 - Automated skull stripping in mouse fMRI analysis using 3D U-Net JF - bioRxiv DO - 10.1101/2021.10.08.462356 SP - 2021.10.08.462356 AU - Guohui Ruan AU - Jiaming Liu AU - Ziqi An AU - Kaiibin Wu AU - Chuanjun Tong AU - Qiang Liu AU - Ping Liang AU - Zhifeng Liang AU - Wufan Chen AU - Xinyuan Zhang AU - Yanqiu Feng Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/10/09/2021.10.08.462356.abstract N2 - 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 identical to 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.Competing Interest StatementThe authors have declared no competing interest. ER -