Abstract
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.
Abstract (translated)
肿瘤分割是评估患者癌症进展的重要步骤。然而，手动分割需要大量专业时间在3D多模态脑部MRI扫描中准确地识别肿瘤。这种对手动分割的依赖使得过程容易受到内和间观察者变异性。本文提出了一种作为 BraTS-GoAT 挑战的一部分的脑肿瘤分割方法。任务是从各种人群中自动分割脑MRI扫描中的肿瘤，包括成人、儿科和欠发达的撒哈拉以南非洲。我们采用最近的一个卷积神经网络架构——MedNeXt 作为基础，并对推理进行 extensive model ensemble 和 postprocessing。我们的实验结果表明，我们的方法在未见过的验证集上的平均DSC为85.54%和HD95为27.88。代码可以在这个 https:// URL 上找到。
URL
https://arxiv.org/abs/2405.02852
