A multimodal multi-slice cooperative segmentation network for stroke lesions

Segmentation of stroke lesions plays an important role in the diagnosis and treatment of computer-aided brain diseases. However, due to the complex relationships between the modes, convolutional neural networks (CNN) using a simple multi-modal fusion strategy are difficult to learn complex and nonlinear complementary information between the modes. On the other hand, with the rapid development of CNN, codec structure has shown great potential in the field of medical image segmentation, but it also exposes corresponding problems. Two-dimensional CNN ignores the information between the slice sequences of medical images, while three-dimensional CNN has a high demand for computing resources. At the same time, the undifferentiated feature aggregation can easily lead to the problem of feature dislocation. To solve these problems, this paper proposes an automatic segmentation network based on multi-mode multi-slice cooperation. In this network, we first design a multi-branch parallel processing strategy, and divide the network into three parallel parts according to different processing data, so as to realize collaborative learning among multi-slices innovatively. Secondly, a convolutional LSTM module with attention mechanism is introduced to directly use the correlation between slices. Third, a cross-modal feature interaction module is developed to adaptively and efficiently fuse and refine multi-modal features. The proposed algorithm was validated on the public stroke dataset ISLES2015, and the experimental results show that the proposed algorithm achieves accurate and effective multimodal stroke segmentation.