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The morbidity of brain stroke increased rapidly in the past few years. Tohelp specialists in lesion measurements and treatment planning, automaticsegmentation methods are critically required for clinical practices. Recently,approaches based on deep learning and methods for contextual informationextraction have served in many image segmentation tasks. However, theirperformances are limited due to the insufficient training of a large number ofparameters, which sometimes fail in capturing long-range dependencies. Toaddress these issues, we propose a depthwise separable convolution based X-Netthat designs a nonlocal operation namely Feature Similarity Module (FSM) tocapture long-range dependencies. The adopted depthwise convolution allows toreduce the network size, while the developed FSM provides a more effective,dense contextual information extraction and thus facilitates bettersegmentation. The effectiveness of X-Net was evaluated on an open datasetAnatomical Tracings of Lesions After Stroke (ATLAS) with superior performanceachieved compared to other six state-of-the-art approaches. We make our codeand models available at https://github.com/Andrewsher/X-Net.

X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies