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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.
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Zhiguo Zhou, Rongfang Wang, Jing Yang, Rongbin Xu, 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