Iterative Seeded Region Growing for Brain Tissue Segmentation
Ke Zhang, Fei Wu, Junxiao Sun, Guanyu Yang, Huazhong Shu, Youyong Kong · 2022 IEEE International Conference on Image Processing (ICIP) · 2022
Brain tissue segmentation from magnetic resonance imaging (MRI) is of significant importance for clinical application and cognitive research. The promising deep learning based methods heavily depend on the quality and quantity of training datasets, and also ignore the domain knowledge. To overcome this issue, this paper proposes a novel Iterative Seeded Region Growing (ISRG) approach for brain tissue segmentation with only one reference image. After super-voxel generation and matching, we first select the high confidence seeded regions based on the high similarity between individual brain images. Then, we obtain initial the voxel-wise tissue probabilities with a proposed fully convolutional network (named TPUNet). Thirdly, the seeded regions are updated according to the voxel-wise tissue probabilities. The second and the third steps are iteratively performed until the segmentation labels of the entire image are obtained. The proposed approach is evaluated on IBSR18 dataset and achieves better results compared with other methods.