Abstract
It is a challenging task to segment brain tumors from multi-modality MRI scans. How to segment and reconstruct brain tumors more accurately and faster remains an open question. The key is to effectively model spatial-temporal information that resides in the input volumetric data. In this paper, we propose Multi-View Pointwise U-Net (MVP U-Net) for brain tumor segmentation. Our segmentation approach follows encoder-decoder based 3D U-Net architecture, among which, the 3D convolution is replaced by three 2D multi-view convolutions in three orthogonal views (axial, sagittal, coronal) of the input data to learn spatial features and one pointwise convolution to learn channel features. Further, we modify the Squeeze-and-Excitation (SE) block properly and introduce it into our original MVP U-Net after the concatenation section. In this way, the generalization ability of the model can be improved while the number of parameters can be reduced. In BraTS 2020 testing dataset, the mean Dice scores of the proposed method were 0.715, 0.839, and 0.768 for enhanced tumor, whole tumor, and tumor core, respectively. The results show the effectiveness of the proposed MVP U-Net with the SE block for multi-modal brain tumor segmentation.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant No. 61903336, 61703369, 61976190, Natural Science Foundation of Zhejiang Province under Grant No. LY21F030015, Key Research and Development Program of Zhejiang Province under Grant No. 2020C03070, Major Science & Technology Projects of Wenzhou under Grant No ZS2017007.
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Zhao, C., Zhao, Z., Zeng, Q., Feng, Y. (2021). MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_9
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