摘要翻译：
有监督图像分割将图像体素分配给一组标签，由特定的标签协议定义。在本文中，我们将分割分解为两个步骤。第一步是我们所说的“基元分割”，其中形成训练数据中可用的各种分割标签的子部分（基元）的体素被分组在一起。第二步涉及基于基元分割计算特定于协议的标签映射。我们的核心贡献是为第一步提供了一个新的损失函数，其中训练了一个原始的分割模型。所提出的损失函数是在基元分割条件下的（协议特定的）“地面真值”标记映射的熵。条件熵损失使训练数据集能够组合使用不同协议手动标记的训练数据集。此外，正如我们的经验所示，它通过一个轻量级协议适应模型促进了一种有效的迁移学习策略，该模型可以用很少的人工标记数据进行训练。我们将所提出的方法应用于大脑MRI扫描的体积分割，在那里我们取得了有希望的结果。
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英文标题：
《Conditional Entropy as a Supervised Primitive Segmentation Loss Function》
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作者：
Sundaresh Ram and Mert R. Sabuncu
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最新提交年份：
2018
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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：
  Supervised image segmentation assigns image voxels to a set of labels, as defined by a specific labeling protocol. In this paper, we decompose segmentation into two steps. The first step is what we call "primitive segmentation", where voxels that form sub-parts (primitives) of the various segmentation labels available in the training data, are grouped together. The second step involves computing a protocol-specific label map based on the primitive segmentation. Our core contribution is a novel loss function for the first step, where a primitive segmentation model is trained. The proposed loss function is the entropy of the (protocol-specific) "ground truth" label map conditioned on the primitive segmentation. The conditional entropy loss enables combining training datasets that have been manually labeled with different protocols. Furthermore, as we show empirically, it facilitates an efficient strategy for transfer learning via a lightweight protocol adaptation model that can be trained with little manually labeled data. We apply the proposed approach to the volumetric segmentation of brain MRI scans, where we achieve promising results.
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PDF链接：
https://arxiv.org/pdf/1805.02852