摘要翻译：
目的：脑积水是脑脊液(CSF)在脑内异常积聚的一种疾病。将脑图像分割为脑组织和脑脊液（手术前和手术后，即术前和术后）在评估手术治疗中起着至关重要的作用。手术前图像的分割通常是一个相对简单的问题，并且已经得到了很好的研究。然而，由于解剖结构扭曲和硬膜下血肿聚集压迫大脑，手术后（术后）计算机断层扫描的分割变得更加困难。大多数基于强度和特征的分割方法都不能将硬膜下与脑和脑脊液分开，因为不同患者的硬膜下几何形状有很大的差异，其强度随时间的变化而变化。我们通过一种学习方法来解决这个问题，该方法将分割视为像素级的监督分类，即使用带有标记像素身份的CT扫描训练集。方法：我们的贡献包括：1.）一个字典学习框架，学习类（段）特定的字典，这些字典可以有效地表示来自同一类的测试样本，而不能很好地表示来自其他类的相应样本相关计算和内存占用的量化，以及3.）用于分割术后脑积水CT图像的定制训练和测试程序。结果：在乌干达CURE儿童医院获得的婴儿CT脑图像上进行的实验表明，我们的方法与现有的替代方案相比是成功的。我们还证明，所提出的算法计算量较少，对训练样本数表现出良好的退化，增强了其部署潜力。
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英文标题：
《Learning Based Segmentation of CT Brain Images: Application to
  Post-Operative Hydrocephalic Scans》
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作者：
Venkateswararao Cherukuri, Peter Ssenyonga, Benjamin C. Warf, Abhaya
  V. Kulkarni, Vishal Monga, Steven J. Schiff
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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：
  Objective: Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF (before and after surgery, i.e. pre-op vs. postop) plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-operative (post-op) computational tomographic (CT)-scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity and feature based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e. a training set of CT scans with labeled pixel identities is employed. Methods: Our contributions include: 1.) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes, 2.) quantification of associated computation and memory footprint, and 3.) a customized training and test procedure for segmenting post-op hydrocephalic CT images. Results: Experiments performed on infant CT brain images acquired from the CURE Children's Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against number of training samples, enhancing its deployment potential.
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PDF链接：
https://arxiv.org/pdf/1712.03993