Abstract
Segmentation is a routine and crucial procedure for the treatment of brain tumors. Deep learning based brain tumor segmentation methods have achieved promising performance in recent years. However, to pursue high segmentation accuracy, most of them require too much memory and computation resources. Motivated by a recently proposed partially reversible U-Net architecture that pays more attention to memory footprint, we further present a novel Memory-Efficient Cascade 3D U-Net (MECU-Net) for brain tumor segmentation in this work, which can achieve comparable segmentation accuracy with less memory and computation consumption. More specifically, MECU-Net utilizes fewer down-sampling channels to reduce the utilization of memory and computation resources. To make up the accuracy loss, MECU-Net employs multi-scale feature fusion module to enhance the feature representation capability. Additionally, a light-weight cascade model, which resolves the problem of small target segmentation accuracy caused by model compression to some extent, is further introduced into the segmentation network. Finally, edge loss and weighted dice loss are combined to refine the brain tumor segmentation results. Experiment results on BraTS 2019 validation set illuminate that MECU-Net can achieve average Dice coefficients of 0.902, 0.824 and 0.777 on the whole tumor, tumor core and enhancing tumor, respectively.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 61972062, the Program for Changjiang Scholars and Innovative Research Team in University under Grant IRT_15R07, the National Key R&D Program of China under Grant 2018YFC0910506, the Natural Science Foundation of Liaoning Province under Grant 2019-MS-011, the Key R&D Program of Liaoning Province under Grant 2019JH2/10100030, the High-level Talent Innovation Support Program of Dalian City under Grant 2016RQ078 and the Liaoning BaiQianWan Talents Program.
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Cheng, X., Jiang, Z., Sun, Q., Zhang, J. (2020). Memory-Efficient Cascade 3D U-Net for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11992. Springer, Cham. https://doi.org/10.1007/978-3-030-46640-4_23
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