论文标题
分析基于深度学习的脑肿瘤分割的MRI模式缺失
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities
论文作者
论文摘要
该技术报告对现有的深度学习（DL）方法进行了比较分析，用于脑肿瘤细分，而MRI模式缺失。评估的方法包括对抗性共同训练网络（ACN）以及MMGAN和DEEPMEDIC的组合。 MMGAN的更稳定且易于使用的版本也在GitHub存储库中开源。使用BRATS2018数据集，这项工作表明，最先进的ACN表现更好，尤其是在缺少T1C时。当仅缺少一种MRI模态时，MMGAN和DEEPMEDIC的简单组合也显示出强大的潜力。此外，这项工作还与未来的研究方向进行了讨论，以进行脑肿瘤分割，而MRI模式缺失。
This technical report presents a comparative analysis of existing deep learning (DL) based approaches for brain tumor segmentation with missing MRI modalities. Approaches evaluated include the Adversarial Co-training Network (ACN) and a combination of mmGAN and DeepMedic. A more stable and easy-to-use version of mmGAN is also open-sourced at a GitHub repository. Using the BraTS2018 dataset, this work demonstrates that the state-of-the-art ACN performs better especially when T1c is missing. While a simple combination of mmGAN and DeepMedic also shows strong potentials when only one MRI modality is missing. Additionally, this work initiated discussions with future research directions for brain tumor segmentation with missing MRI modalities.
