3D Randomized Connection Network With Graph-Based Label Inference

In this paper, a novel 3D deep learning network is proposed for brain magnetic resonance image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity. The convolutional long-short term memory and the 3D convolution are employed as network units to capture the long-term and short-term 3D properties, respectively. To assemble these two kinds of spatial-temporal information and refine the deep learning outcomes, we further introduce an efficient graph-based node selection and label inference method. Experiments have been carried out on two publicly available databases and results demonstrate that the proposed method can obtain competitive performances as compared with the other state-of-the-art methods.