MSDF-Net: Multi-Scale Deep Fusion Network for Stroke Lesion Segmentation

Abstract

Lesion segmentation is of great research interest due to its capability in facilitating accurate stroke diagnosis and surgical planning. Existing deep neural networks, such as U-net, have demonstrated encouraging progress in biomedical image segmentation. Nevertheless, there are still many challenges related to the segmentation of stroke lesions, including dealing with diverse lesion locations, variations in lesion scales, and fuzzy lesion boundaries. In order to address these challenges, this paper proposes a deep neural network architecture denoted as the Multi-Scale Deep Fusion Network (MSDF-Net) with Atrous Spatial Pyramid Pooling (ASPP) for the feature extraction at different scales, and the inclusion of capsules to deal with complicated relative entities. The proposed method is essentially an end-to-end deep encoder-decoder neural network. The cross connection between the encoder and the decoder guarantees the high resolution of the feature mapping. Experimental results on the open-source Anatomical Tracings of Lesions After Stroke (ATLAS) dataset shows that the proposed model achieved a higher evaluating score compared to 5 existing models.