Extending nn-UNet for Brain Tumor Segmentation

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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 ).
Publisher
Springer Science and Business Media Deutschland GmbH
Issue Date
2021-09
Language
English
Citation

7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, pp.173 - 186

ISSN
0302-9743
DOI
10.1007/978-3-031-09002-8_16
URI
http://hdl.handle.net/10203/299633
Appears in Collection
BiS-Conference Papers(학술회의논문)
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