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Accurate segmentation of brain tissue in magnetic resonance images (MRI) is adiffcult task due to different types of brain abnormalities. Using informationand features from multimodal MRI including T1, T1-weighted inversion recovery(T1-IR) and T2-FLAIR and differential geometric features including the Jacobiandeterminant(JD) and the curl vector(CV) derived from T1 modality can result ina more accurate analysis of brain images. In this paper, we use thedifferential geometric information including JD and CV as image characteristicsto measure the differences between different MRI images, which represent localsize changes and local rotations of the brain image, and we can use them as oneCNN channel with other three modalities (T1-weighted, T1-IR and T2-FLAIR) toget more accurate results of brain segmentation. We test this method on twodatasets including IBSR dataset and MRBrainS datasets based on the deepvoxelwise residual network, namely VoxResNet, and obtain excellent improvementover single modality or three modalities and increases averageDSC(Cerebrospinal Fluid (CSF), Gray Matter (GM) and White Matter (WM)) by about1.5% on the well-known MRBrainS18 dataset and about 2.5% on the IBSR dataset.Moreover, we discuss that one modality combined with its JD or CV informationcan replace the segmentation effect of three modalities, which can providemedical conveniences for doctor to diagnose because only to extract T1-modalityMRI image of patients. Finally, we also compare the segmentation performance ofour method in two networks, VoxResNet and U-Net network. The results showVoxResNet has a better performance than U-Net network with our method in brainMRI segmentation. We believe the proposed method can advance the performance inbrain segmentation and clinical diagnosis.

The Method of Multimodal MRI Brain Image Segmentation Based on Differential Geometric Features