Brain tumor segmentation is essential for the diagnosis and prognosis of patients with gliomas. The brain tumor segmentation challenge has continued to provide a great source of data to develop automatic algorithms to perform 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 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 as well as online evaluation from the organizers showed the effectiveness of our approach, with minor improvement in quantitative metrics when compared to the baseline. The proposed models won first place in the final ranking on unseen test data. The codes, pretrained weights, and docker image for the winning submission are publicly available at https://github.com/rixez/Brats21_KAIST_MRI_Lab
翻译:脑肿瘤分解对于诊断和预测具有显微瘤的患者至关重要。脑肿瘤分解挑战继续提供巨大的数据来源,以开发用于执行任务的自动算法。本文描述了我们对2021年竞争的贡献。我们根据去年竞争的胜利进入点nn-UNet开发了我们的方法。我们尝试了几项修改,包括使用更大的网络,以群体正常化取代批量正常化,以及在解码器中利用轴心。内部5倍交叉验证以及组织者在线评估显示了我们的方法的有效性,与基线相比,定量指标略有改进。提议的模型在不可见测试数据的最后排名中获得了第一位。成功提交的代码、预选重量和 docker 图像可在 https://github.com/rixez/Brats21_KAIST_MRI_Lab 上公开查阅。