Abstract
Segmentation networks with encoder and decoder structures provide remarkable results in the segmentation of gliomas MRI. However, the network loses small-scale tumor feature information during the encoding phase due to the limitations of the traditional 3 × 3 convolutional layer, decreasing network segmentation accuracy. We designed a three-branch convolution module (TBC module) to replace the traditional convolutional layer to address the problem of small-scale tumor information loss. The TBC module is divided into three branches, each of which extracts image features using a different convolutional approach before fusing the three branches’ features as the TBC module’s output. The TBC module enables the model to learn richer small-scale tumor features during encoding. Furthermore, since the tumor area in an MRI only accounts for around 2% of the whole image, there is a problem with pixel category imbalance. We construct a new loss function to address the problem of category imbalance. Extensive experiments on BraTS datasets demonstrate that the proposed method achieves very competitive results with the state-of-the-art approaches.
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Acknowledgements
The work was supported by the High-level Talents Fund of Hubei University of Technology under grant No. GCRC2020016, Open Funding Project of the State Key Laboratory of Biocatalysis and Enzyme Engineering No. SKLBEE2020020 and SKLBEE2021020.
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Yang, Y., Gan, H., Yang, Z. (2022). TBC-Unet: U-net with Three-Branch Convolution for Gliomas MRI Segmentation. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_5
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