Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Method of Multimodal MRI Brain Image Segmentation Based on
  Differential Geometric Features   Accurate segmentation of brain tissue in magnetic resonance images (MRI) is a
diffcult task due to different types of brain abnormalities. Using information
and features from multimodal MRI including T1, T1-weighted inversion recovery
(T1-IR) and T2-FLAIR and differential geometric features including the Jacobian
determinant(JD) and the curl vector(CV) derived from T1 modality can result in
a more accurate analysis of brain images. In this paper, we use the
differential geometric information including JD and CV as image characteristics
to measure the differences between different MRI images, which represent local
size changes and local rotations of the brain image, and we can use them as one
CNN channel with other three modalities (T1-weighted, T1-IR and T2-FLAIR) to
get more accurate results of brain segmentation. We test this method on two
datasets including IBSR dataset and MRBrainS datasets based on the deep
voxelwise residual network, namely VoxResNet, and obtain excellent improvement
over single modality or three modalities and increases average
DSC(Cerebrospinal Fluid (CSF), Gray Matter (GM) and White Matter (WM)) by about
1.5% 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 information
can replace the segmentation effect of three modalities, which can provide
medical conveniences for doctor to diagnose because only to extract T1-modality
MRI image of patients. Finally, we also compare the segmentation performance of
our method in two networks, VoxResNet and U-Net network. The results show
VoxResNet has a better performance than U-Net network with our method in brain
MRI segmentation. We believe the proposed method can advance the performance in
brain segmentation and clinical diagnosis.

3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg