Application of Three-Dimensional Convolution Network in Brain Hippocampus Segmentation

Abstract

In order to improve the accuracy and robustness of hippocampus segmentation, a new three-dimensional convolutional network named Dilated-3DUnet is proposed. The number of channels in the convolution layer of the network adopts the “Pyramid” distribution method, which effectively reduces the size of the parameters. In addition,using 3D dilated convolution as cascading convolution operation not only effectively combines the deep and shallow features of brain MRI (magnetic resonance imaging) images， but also expands the receptive field of convolution without changing the number of parameters. Multi-scale information is obtained, which can better capture the shallow features of the brain MRI image, so as to improve the segmentation accuracy. Experiments are carried out on the ADNI dataset, using dice similarity coefficient, sensitivity and predictive positivity value as evaluation indexes, and the accuracy reaches 89.32%, 88.72% and 90.05%, respectively. Experiments show that Dilated-3DUnet makes full use of the three-dimensional spatial information of brain MRI images, which has stronger generalization ability and better feature expression ability, thus greatly improving the segmentation accuracy.

Keywords

• hippocampus segmentation
• brain magnetic resonance imaging (mri)
• convolutional network
• dilated convolution