Triple Attention Segmentation Network for Brain Tumor Images
Han Yang1,2,3, Song Jinmiao2,3, Xue Anyi1, Duan Xiaodong1,2,3*
1(College of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, Liaoning, China) 2(SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, Dalian 116600, Liaoning, China) 3(Dalian Key Lab of Digital Technology for National Culture, Dalian Minzu University, Dalian 116600, Liaoning, China)
Abstract：Brain tumor segmentation is the basis of clinical routine and treatment of brain tumor diseases with computer-aided diagnosis. In this paper, we proposed a triple attention segmentation network based on brain tumor images, aiming to solve the problems of the current brain tumor MRI image segmentation network that has too many layers and is lack of connection between local and global feature information, which leads to the reduction of image segmentation accuracy. First, inspired by the residual network, we replaced the convolution module both in the encoding and decoding layer of the original segmentation network with a deep residual module to solve the problem of gradient disappearance caused by network deepening. Next, by introducing a triple attention module to combine local and global image features, the network was able to learn important image features better and improved the network's segmentation accuracy of brain tumor images. Finally, The improved network was evaluated by the Dice coefficient, and other brain tumor indicators were adopted on the BraTS brain tumor MRI image datasets released by the MICCAI competition includes 335 patient cases, among which the whole brain tumor score reached 85.20%, the brain tumor core score reached 87.10%, and the enhanced brain tumor area score reached 80.80%. Experimental results showed that the proposed segmentation network increased the segmentation performance of brain tumor MRI images without increasing the training time.
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