Abstract
Tumor segmentation of brain MRI image is an important and challenging computer vision task. With well-curated multi-institutional multi-parametric MRI (mpMRI) data, the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021 is a great bench-marking venue for world-wide researchers to contribute to the advancement of the state-of-the-art. HarDNet is a memory-efficient neural network backbone that has demonstrated excellent performance and efficiency in image classification, object detection, real-time semantic segmentation, and colonoscopy polyp segmentation. In this paper, we propose HarDNet-BTS, a U-Net-like encoder-decoder architecture with HarDNet backbone, for Brain Tumor Segmentation. We train it with the BraTS 2021 dataset using three training strategies and ensemble the resultant models to improve the prediction quality. Assessment reports from the BraTS 2021 validation server show that HarDNet-BTS delivers state-of-the-art performance (Dice_ET = 0.8442, Dice_TC = 0.8793, Dice_WT = 0.9260, HD95_ET = 12.592, HD95_TC = 7.073, HD95_WT = 3.884). It was ranked 8th in the validation phase. Its performance on the final testing dataset is consistent with that of the validation phase (Dice_ET = 0.8727, Dice_TC = 0.8665, Dice_WT = 0.9286, HD95_ET = 8.496, HD95_TC = 18.606, HD95_WT = 4.059). Inferencing an MRI case takes only 16 s of GPU time and 6GBs of GPU memory.
Supported in part by the Ministry of Science and Technology, TAIWAN.
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Acknowledgements
We would like to thank Mr. Ping Chao and Mr. James Huang for their open-sourced HarDNet and HarDNet-MSEG, respectively. We would also like to thank Mr. Wei-Xiang Kuo, Mr. Kao-chun Pan and Mr. Kuan-Ying Lai for many fruitful discussions. This research is supported in part by a grant (no. 110-2218-E-007-043) from the Ministry of Science and Technology (MOST) of Taiwan. We thank the National Center for High-performance Computing (NCHC) for providing computational and storage resources. Without it this research is impossible.
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Wu, HY., Lin, YL. (2022). HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12962. Springer, Cham. https://doi.org/10.1007/978-3-031-08999-2_21
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