• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Conference on Medical Imaging 2008: Computer-Aided Diagnosis; 20080219-21; San Diego,CA(US) >A Met a-Classifier for Detecting Prostate Cancer by Quantitative Integration of In Vivo Magnetic Resonance Spectroscopy and Magnetic Resonance Imaging
【24h】

A Met a-Classifier for Detecting Prostate Cancer by Quantitative Integration of In Vivo Magnetic Resonance Spectroscopy and Magnetic Resonance Imaging

机译：一种通过体内磁共振波谱和磁共振成像定量积分检测前列腺癌的Met分类器

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Recently, in vivo Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) have emerged as promising new modalities to aid in prostate cancer (CaP) detection. MRI provides anatomic and structural information of the prostate while MRS provides functional data pertaining to biochemical concentrations of metabolites such as creatine, choline and citrate. We have previously presented a hierarchical clustering scheme for CaP detection on in vivo prostate MRS and have recently developed a computer-aided method for CaP detection on in vivo prostate MRI. In this paper we present a novel scheme to develop a meta-classifier to detect CaP in vivo via quantitative integration of multimodal prostate MRS and MRI by use of non-linear dimensionality reduction (NLDR) methods including spectral clustering and locally linear embedding (LLE). Quantitative integration of multimodal image data (MRI and PET) involves the concatenation of image intensities following image registration. However multimodal data integration is non-trivial when the individual modalities include spectral and image intensity data. We propose a data combination solution wherein we project the feature spaces (image intensities and spectral data) associated with each of the modalities into a lower dimensional embedding space via NLDR. NLDR methods preserve the relationships between the objects in the original high dimensional space when projecting them into the reduced low dimensional space. Since the original spectral and image intensity data are divorced from their original physical meaning in the reduced dimensional space, data at the same spatial location can be integrated by concatenating the respective embedding vectors. Unsupervised consensus clustering is then used to partition objects into different classes in the combined MRS and MRI embedding space. Quantitative results of our multimodal computer-aided diagnosis scheme on 16 sets of patient data obtained from the ACRIN trial, for which corresponding histological ground truth for spatial extent of CaP is known, show a marginally higher sensitivity, specificity, and positive predictive value compared to corresponding CAD results with the individual modalities.
机译：最近，体内磁共振成像（MRI）和磁共振波谱学（MRS）出现，有望成为有助于前列腺癌（CaP）检测的新方法。 MRI提供前列腺的解剖和结构信息，而MRS提供与代谢物（如肌酸，胆碱和柠檬酸盐）的生化浓度有关的功能数据。我们先前已经提出了用于在体内前列腺MRS上进行CaP检测的分层聚类方案，并且最近开发了一种用于在体内前列腺MRI上进行CaP检测的计算机辅助方法。在本文中，我们提出了一种新颖的方案，该方案通过使用非线性降维（NLDR）方法（包括光谱聚类和局部线性嵌入（LLE）），通过多模式前列腺MRS和MRI的定量整合，开发了一种用于在体内检测CaP的元分类器。 。多峰图像数据（MRI和PET）的定量整合涉及图像配准后图像强度的级联。但是，当各个模态包括光谱和图像强度数据时，多模态数据集成是不平凡的。我们提出了一种数据组合解决方案，其中我们将与每个模态关联的特征空间（图像强度和光谱数据）通过NLDR投影到较低维的嵌入空间中。 NLDR方法将对象投影到缩小的低维空间中时，可以保留原始高维空间中的对象之间的关系。由于原始光谱和图像强度数据在降维空间中与它们的原始物理意义不同，因此可以通过合并各个嵌入向量来集成相同空间位置的数据。然后使用无监督共识聚类在MRS和MRI嵌入空间的组合中将对象划分为不同的类别。我们的多模式计算机辅助诊断方案从ACRIN试验获得的16组患者数据的定量结果（相对于CaP的空间范围而言，其相应的组织学依据是已知的），与之相比，其敏感性，特异性和阳性预测值略高。相应的CAD结果以及各个模式。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号