Brain tumor segmentation is a fundamental step in assessing a patient's
cancer progression. However, manual segmentation demands significant expert
time to identify tumors in 3D multimodal brain MRI scans accurately. This
reliance on manual segmentation makes the process prone to intra- and
inter-observer variability. This work proposes a brain tumor segmentation
method as part of the BraTS-GoAT challenge. The task is to segment tumors in
brain MRI scans automatically from various populations, such as adults,
pediatrics, and underserved sub-Saharan Africa. We employ a recent CNN
architecture for medical image segmentation, namely MedNeXt, as our baseline,
and we implement extensive model ensembling and postprocessing for inference.
Our experiments show that our method performs well on the unseen validation set
with an average DSC of 85.54% and HD95 of 27.88. The code is available on
this https URL

通过使用 MedNeXt 等卷积神经网络架构进行脑肿瘤分割，本研究在 BraTS-GoAT 挑战中的各种人群（如成人、儿科和非洲撒哈拉以南地区）的脑部 MRI 扫描图像中自动分割肿瘤，并通过大量的模型集成和后处理方法在未知验证集上表现良好，平均 DSC 为 85.54％，HD95 为 27.88。