乳腺非肿块增强肿瘤的自动检测与分割 动态增强磁共振成像

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摘要翻译：
非肿块增强病变(NME)是乳腺动态增强磁共振成像（DCE-MRI）诊断的一个挑战。计算机辅助诊断(CAD)系统为医生提供了先进的分析、评估和评价工具，这些工具对诊断性能有重要影响。在这里，我们提出了一种新的方法来解决NME检测和分割的挑战，利用独立分量分析(ICA)来提取数据驱动的动态损伤特征。从乳腺患者的DCE-MRI数据集中获得一组独立的数据源，用多条动态曲线描述不同组织的动态行为，并用一组特征图像描述每个体素的得分。利用分解矩阵将一幅新的测试图像投影到独立的源空间中，并用人工绘制的数据训练支持向量机(SVM)对每个体素进行分类。通过控制SVM超平面定位，提出了一种解决高误报率问题的方法，优于已有的方法。
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英文标题：
《Automated detection and segmentation of non-mass enhancing breast tumors
  with dynamic contrast-enhanced magnetic resonance imaging》
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作者：
Ignacio Alvarez Illan, Javier Ramirez, Juan M. Gorriz, Maria Adele
  Marino, Daly Avenda\~no, Thomas Helbich, Pascal Baltzer, Katja Pinker, Anke
  Meyer-Baese
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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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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  Non-mass enhancing lesions (NME) constitute a diagnostic challenge in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer Aided Diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from DCE-MRI dataset of breast patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false positive rate problem is proposed by controlling the SVM hyperplane location, outperforming previously published approaches.
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PDF链接：
https://arxiv.org/pdf/1803.042

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关键词：磁共振 Segmentation Architecture Construction Mathematical 独立 方法 挑战 解决 imaging