Multimodal weighted network for 3D brain tumor segmentation in MRI images

Zhiguo Zhou; Rongfang Wang; Jing Yang; Rongbin Xu; Jinkun Guo

doi:10.1117/12.2580879

15 February 2021 Multimodal weighted network for 3D brain tumor segmentation in MRI images

Zhiguo Zhou, Rongfang Wang, Jing Yang, Rongbin Xu, Jinkun Guo

Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 116001O (2021) https://doi.org/10.1117/12.2580879
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Accurately brain tumor segmentation is critical on treatment plan making and treatment outcome prediction. Manually segmenting tumor is tedious and time consuming. Therefore, developing a reliable and automatic brain tumor segmentation model is necessary. In this study, we developed a new multimodal weighted network (MW-Net), which fully utilizes the biological information from multiple modalities. Since the contribution from different modality is different, the relative weight in introduced into MW-Net and trained as the hyperparameter with other parameters in an end-to-end way. The 3D segmentation results can be directly obtained in testing stage. The experimental results showed MW-Net outperformed 3D-U-Net.

Conference Presentation

Citation Download Citation

Zhiguo Zhou, Rongfang Wang, Jing Yang, Rongbin Xu, and Jinkun Guo "Multimodal weighted network for 3D brain tumor segmentation in MRI images", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116001O (15 February 2021); https://doi.org/10.1117/12.2580879

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