Hybrid Feature Network Driven by Attention and Graph Features for Multiple Sclerosis Lesion Segmentation from MR Images

Accurate segmentation of multiple sclerosis from MR images, faces the challenges imposed by the high variability in lesion appearance, and distant and disjoint lesion regions. Previous methods using multi-scale feature fusion or cascade networks, mostly rely on local feature representation learned from limited receptive field, which fail to leverage global context and model relations between multiple regions. To address these issues, we propose a hybrid feature network (HF-Net) driven by attention and graph convolution features, to improve the MS lesion segmentation from MR images. The attention features help to enhance discriminative feature representation. Specifically, the pyramid augmented attention module encodes spatial features into local features, while the channel augmented attention module models channel-wise interdependencies between features. Meanwhile, the graph feature module exploits the global relations between features over local receptive field. The proposed HF-Net was evaluated on the datasets from the MSSEG Challenge and the ongoing ISBI Challenge, which outperforms several state-of-the-art methods.