摘要翻译：
卷积神经网络(CNN)用于医学成像的瓶颈是训练所需的注释数据的数量。人工分割被认为是“黄金标准”。然而，专家手工分割的医学图像数据很少，因为这一步骤耗时且昂贵。在这项工作中，我们提出了在基于深度学习的头骨剥离也称为脑提取中使用我们所说的银标准掩模来进行数据增强。我们使用一致性算法同时真值和性能水平估计(STAPLE)生成银标准掩码。我们评估了由银标准面具和金标准面具生成的CNN模型。然后，我们在一个数据集上验证了用于CNNs训练的银标准面具，并展示了它对其他两个数据集的推广。我们的结果表明，用银标准掩模生成的模型与用金标准掩模生成的模型相当，并且具有更好的推广性。此外，我们的结果还表明，银标准掩码可以用来在训练阶段增加输入数据集，减少在这一步对人工分割的需要。
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英文标题：
《Silver Standard Masks for Data Augmentation Applied to
  Deep-Learning-Based Skull-Stripping》
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作者：
Oeslle Lucena, Roberto Souza, Let\'icia Rittner, Richard Frayne,
  Roberto Lotufo
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the "gold-standard". However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. We propose in this work the use of what we refer to as silver standard masks for data augmentation in deep-learning-based skull-stripping also known as brain extraction. We generated the silver standard masks using the consensus algorithm Simultaneous Truth and Performance Level Estimation (STAPLE). We evaluated CNN models generated by the silver and gold standard masks. Then, we validated the silver standard masks for CNNs training in one dataset, and showed its generalization to two other datasets. Our results indicated that models generated with silver standard masks are comparable to models generated with gold standard masks and have better generalizability. Moreover, our results also indicate that silver standard masks could be used to augment the input dataset at training stage, reducing the need for manual segmentation at this step.
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PDF链接：
https://arxiv.org/pdf/1710.08354