CA-Res2UNet++: a deep residual UNet-based method for brain tumor segmentation in multimodal MRI

Yujiao Hu; Weiting Chen; Wenjing Wu; Jianghao Long

doi:10.1117/12.2687868

28 August 2023 CA-Res2UNet++: a deep residual UNet-based method for brain tumor segmentation in multimodal MRI

Yujiao Hu, Weiting Chen, Wenjing Wu, Jianghao Long

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 1272429 (2023) https://doi.org/10.1117/12.2687868
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

In recent years, segmentation of the multimodal brain tumor image puts forward high requirements for performance. To meet the accuracy requirements, we propose a multimodal brain tumor image segmentation method based on UNet and deep residual learning, which can make full use of multimodal information in order to obtain details. The model achieves the information extraction and integration of multimodal images at different scales by introducing a multiscale feature extraction module, a coordinate attention module, and a dense atrous spatial pyramid pooling module. The results show that the proposed method can fully exploit the multi-scale context information of multimodal images, alleviate the problem of blurred edges in segmentation regions, and achieve good performance on popular evaluation metrics.

Citation Download Citation

Yujiao Hu, Weiting Chen, Wenjing Wu, and Jianghao Long "CA-Res2UNet++: a deep residual UNet-based method for brain tumor segmentation in multimodal MRI", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 1272429 (28 August 2023); https://doi.org/10.1117/12.2687868

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