RP-Net: A 3D Convolutional Neural Network for Brain Segmentation From Magnetic Resonance Imaging

Abstract

Quantitative analysis of brain volume is quite significant for the diagnosis of brain diseases. Accurate segmentation of essential brain tissues from 3D medical images is fundamental to quantitative brain analysis. Since manual segmentation is extremely tedious and time-consuming, there is a growing demand for automated segmentation. In this paper, we propose a 3D convolutional neural network including recursive residual blocks and a pyramid pooling module (RP-Net) for segmenting brain from 3D magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). RP-Net is an U-Net like the network that consists of a downsampling path and an upsampling path. Each path consists of four stages with a recursive residual block. All layers in RP-Net are implemented in a 3D manner. The pyramid pooling module is applied before a voxel-wise classification layer for obtaining both local and global context information. The RP-Net has been evaluated on WM, GM, and CSF segmentation with CANDI, IBSR18, and IBSR20 dataset. The experiments show that the RP-Net achieved mean dice similarity coefficients of 90.7% on CANDI, 90.49% on IBSR18 and 84.96% on IBSR20. The results demonstrate that our proposed method has achieved a significant improvement in segmentation accuracy compared to other reported approaches.