论文标题
使用强大的损失功能从不完美的培训数据中学习：应用于大脑图像分割
Learning from imperfect training data using a robust loss function: application to brain image segmentation
论文作者
论文摘要
分割是MRI医学图像分析中最重要的任务之一，通常是许多临床应用中的第一个也是最关键的步骤。在大脑MRI分析中，头部分割通常用于测量和可视化大脑的解剖结构，也是其他应用的必要步骤，例如电脑摄影和磁脑摄影（EEG/MEG）中的电流源重建。在这里，我们提出了一个深度学习框架，该框架可以仅使用T1加权MRI作为输入来分割大脑，头骨和颅外组织。此外，我们描述了一种在嘈杂标签的存在下训练模型的强大方法。
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, head segmentation is commonly used for measuring and visualizing the brain's anatomical structures and is also a necessary step for other applications such as current-source reconstruction in electroencephalography and magnetoencephalography (EEG/MEG). Here we propose a deep learning framework that can segment brain, skull, and extra-cranial tissue using only T1-weighted MRI as input. In addition, we describe a robust method for training the model in the presence of noisy labels.
