Abstract
Brain tumor segmentation is essential for the diagnosis and prognosis of patients with gliomas. The brain tumor segmentation challenge has provided an abundant and high-quality data source to develop automatic algorithms for the task. This paper describes our contribution to the 2021 competition. We developed our methods based on nn-UNet, the winning entry of last year’s competition. We experimented with several modifications, including using a larger network, replacing batch normalization with group normalization and utilizing axial attention in the decoder. Internal 5-fold cross-validation and online evaluation from the organizers showed a minor improvement in quantitative metrics compared to the baseline. The proposed models won first place in the final ranking on unseen test data, achieving a dice score of 88.35%, 88.78%, 93.19% for the enhancing tumor, the tumor core, and the whole tumor, respectively. The codes, pretrained weights, and docker image for the winning submission are publicly available. (https://github.com/rixez/Brats21_KAIST_MRI_Lab https://hub.docker.com/r/rixez/brats21nnunet)
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Acknowledgements
We would like to acknowledge Fabian Isensee for his development of the nn-UNet framework and for sharing the models from last year competition.
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Luu, H.M., Park, SH. (2022). Extending nn-UNet 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 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_16
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