New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
Source abstract
Abstract
In the treatment process of brain tumors, it is of great importance to develop a set of MRI image segmentation methods with high accuracy and low cost. In order to extract the feature information for each region of the brain tumor more effectively, this paper proposes a new model Ga-U-Net based on Gabor convolution and an attention mechanism. Based on 3D U-Net, Gabor convolution is added at the shallow layer of the encoder, which is able to learn the local structure and texture information of the tumor better. After that, the CBAM attention mechanism is added after the output of each layer of the encoder, which not only enhances the network’s ability to perceive the brain tumor boundary information but also reduces some redundant information by allocating the attention to the two dimensions of space and channel. Experimental results show that the model performs well for multiple tumor regions (WT, TC, ET) on the brain tumor dataset BraTS 2021, with Dice coefficients of 0.910, 0.897, and 0.856, respectively, which are improved by 0.3%, 2%, and 1.7% compared to the base network, the U-Net network, with an average Dice of 0.887 and an average Hausdorff distance of 9.12, all of which are better than a few other state-of-the-art deep models for biomedical image segmentation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Brain Tumor Detection and Classification · Medical Image Segmentation Techniques · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Funding
Jiangsu University of Science and Technology
References (30)
Show source-provided references
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