Extending nn-UNet for Brain Tumor Segmentation

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 ).