Spatial-channel Relation Learning for Brain Tumor Segmentation

Medical Physics
Guohua ChengZhongxiang Ding

Abstract

Recently, research on brain tumor segmentation has made great progress. However, ambiguous patterns in MRI data and linear fusion omitting semantic gaps between features in different branches remain challenging. We need to design a mechanism to fully utilize the similarity within the spatial space and channel space and the correlation between these two spaces to improve the result of volumetric segmentation. We propose a revised cascade structure network. In each subnetwork, a context exploitation module is introduced between the encoder and decoder, in which the dual attention mechanism is adopted to learn the information within the spatial space and channel space, and space interaction learning is employed to model the relation between the spatial and channel spaces. Extensive experiments on the BraTS19 dataset have evaluated that our approach improves the dice coefficient (DC) by a margin of 2.1, 2.0, and 1.4 for whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively, obtaining results competitive with state-of-art approaches working on brain tumor segmentation. Context exploitation in the embedding feature spaces, including intraspace relations and interspace relations, can effectively model dependency in...Continue Reading

References

Mar 8, 2013·Neural Networks : the Official Journal of the International Neural Network Society·Tohru Nitta
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Aug 27, 2019·IEEE Transactions on Cybernetics·Bin Xue, Ningning Tong
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