体素
计算机科学
人工智能
分割
模式识别(心理学)
图像分割
相似性(几何)
匹配(统计)
脑组织
计算机视觉
图像(数学)
数学
生物医学工程
医学
统计
作者
Ke Zhang,Fei Wu,Junxiao Sun,Guanyu Yang,Huazhong Shu,Youyong Kong
标识
DOI:10.1109/icip46576.2022.9897303
摘要
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.
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