利用卷积神经网络增强跨不同人群的脑肿瘤分割
On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks
AI总结：
本文针对跨人群脑肿瘤自动分割问题，以MedNeXt为基线并结合模型集成与后处理，在BraTS-GoAT挑战中取得平均DSC 85.54%和HD95 27.88的结果。
摘要：
脑肿瘤分割是评估患者癌症进展的基础步骤。然而，手动分割需要大量专家时间才能在3D多模态脑MRI扫描中准确识别肿瘤。这种对手动分割的依赖使得该过程容易产生观察者内和观察者间的差异。本文提出了一种脑肿瘤分割方法，作为BraTS-GoAT挑战赛的一部分。任务是对来自不同人群（如成人、儿童和医疗资源不足的撒哈拉以南非洲地区）的脑MRI扫描进行自动肿瘤分割。我们采用一种近期用于医学图像分割的CNN架构，即MedNeXt，作为基线，并实现了广泛的模型集成和后处理以进行推理。我们的实验表明，该方法在未见过的验证集上表现良好，平均DSC为85.54%，HD95为27.88。代码可在https://github.com/BioMedIA-MBZUAI/BraTS2024_BioMedIAMBZ获取。
英文摘要：
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 https://github.com/BioMedIA-MBZUAI/BraTS2024_BioMedIAMBZ.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
