  In this paper, a novel 3D deep learning network is proposed for brain MR
image segmentation with randomized connection, which can decrease the
dependency between layers and increase the network capacity. The convolutional
LSTM and 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 other state-of-the-art methods.
