Abstract
Semantic segmentation plays an essential role in brain tumor diagnosis and treatment planning. Yet, manual segmentation is a time-consuming task. That fact leads to hire the Deep Neural Networks to segment brain tumor. In this work, we proposed a variety of 3D U-Net, which can achieve comparable segmentation accuracy with less graphic memory cost. To be more specific, our model employs a modified attention block to refine the feature map representation along the skip-connection bridge, which consists of parallelly connected spatial and channel attention blocks. Dice coefficients for enhancing tumor, whole tumor, and tumor core reached 0.752, 0.879 and 0.779 respectively on the BRATS- 2020 valid dataset.
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The project is supported by Sichuan Science and Technology Program. It is partially funded by Grant SCITLAB-0013 of Intelligent Terminal Key Laboratory of SiChuan Province.
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Jun, W., Haoxiang, X., Wang, Z. (2021). Brain Tumor Segmentation Using Dual-Path Attention U-Net in 3D MRI Images. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_17
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DOI: https://doi.org/10.1007/978-3-030-72084-1_17
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