A multi-path adaptive fusion network for multimodal brain tumor segmentation

Abstract

The deep learning method has shown its outstanding performance in object recognition and becomes the first choice for medical image analysis. However, how to effectively propagate features in the learning layer and how to fuse low-level visual features and high-level semantic features, is still a challenging task. In addition, with the rapid development of the neural network, an increasing need of running Convolutional Neural Network (CNN) models with limited computing power and memory resource. To address these problems, this paper proposes a novel multi-path adaptive fusion network. More specifically, we apply the idea of “skip connection” in ResNets to the dense block so as to effectively reserve and propagate more low-level visual features. A contiguous memory mechanism has been realized by adopting direction connections from the state of preceding dense block to all layers of current dense block in the network. Then, a multi-path adaptive fusion dense block was adopted in the up-sampling process to adaptively adjust the low-level visual feature and then to fuse with high-level semantic features. By evaluating the proposed framework on the challenging BRATS2015 dataset, it can be proven that this framework achieves state-of-the-art results by comparing with other counterpart methods. Moreover, parameters of the proposed framework are much less than most published methods.