Integrating Structural and Functional Imaging for Computer Assisted Detection of Prostate Cancer on Multi-Protocol In Vivo 3 Tesla MRI.
Integrating Structural and Functional Imaging for Computer Assisted Detection of Prostate Cancer on Multi-Protocol In Vivo 3 Tesla MRI.
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DOI：
10.1117/12.811899
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发表时间：
2009-02-27
期刊：
影响因子：
--
通讯作者：
Madabhushi A
中科院分区：
文献类型：
--
作者：
Viswanath S;Bloch BN;Rosen M;Chappelow J;Toth R;Rofsky N;Lenkinski R;Genega E;Kalyanpur A;Madabhushi A
前列腺癌（CaP）的筛查和检测目前缺乏基于图像的方案，这反映在目前与盲法六分仪活检相关的高假阴性率。多协议磁共振成像（MRI）提供高分辨率的身体内部结构（如前列腺）的功能和结构数据。在本文中，我们提出了一种新的综合计算机辅助方案，通过整合动态对比增强（DCE）和t2加权（T2-w） MRI分别获得的功能和结构信息，从高分辨率体内多协议MRI中检测CaP。我们的方案是全自动的，包括(a)前列腺分割，(b)多模态图像配准，以及(c)用于信息融合的数据表示和多分类器模块。通过改进的活动形状模型进行前列腺边界分割后，DCE/T2-w方案和T2-w/离体组织学前列腺切除术标本通过可变形的多属性注册方案进行对齐。T2-w/组织学对齐允许将真实的CaP范围映射到体内MRI上，用于多协议MRI CaP分类器的训练和评估。使用的元分类器是由多个决策树分类器组成的随机森林，每个决策树分类器分别在T2-w结构、纹理和DCE功能属性上进行训练。使用来自6个患者数据集的18幅图像，以每像素为基础进行3倍分类器交叉验证。我们的研究结果表明，由T2-w结构纹理数据和DCE功能数据（ROC曲线下面积为0.815）集成得到的CaP检测结果明显优于基于任何一种单独模式的检测结果（0.704 （T2-w）和0.682 (DCE)）。研究还发现，直接在集成的T2-w和DCE数据（数据级集成）上训练的元分类器明显优于通过组合来自单个T2-w和DCE通道的分类器输出构建的决策级元分类器。
Screening and detection of prostate cancer (CaP) currently lacks an image-based protocol which is reflected in the high false negative rates currently associated with blinded sextant biopsies. Multi-protocol magnetic resonance imaging (MRI) offers high resolution functional and structural data about internal body structures (such as the prostate). In this paper we present a novel comprehensive computer-aided scheme for CaP detection from high resolution in vivo multi-protocol MRI by integrating functional and structural information obtained via dynamic-contrast enhanced (DCE) and T2-weighted (T2-w) MRI, respectively. Our scheme is fully-automated and comprises (a) prostate segmentation, (b) multimodal image registration, and (c) data representation and multi-classifier modules for information fusion. Following prostate boundary segmentation via an improved active shape model, the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable, multi-attribute registration scheme. T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI, which is used for training and evaluation of a multi-protocol MRI CaP classifier. The meta-classifier used is a random forest constructed by bagging multiple decision tree classifiers, each trained individually on T2-w structural, textural and DCE functional attributes. 3-fold classifier cross validation was performed using a set of 18 images derived from 6 patient datasets on a per-pixel basis. Our results show that the results of CaP detection obtained from integration of T2-w structural textural data and DCE functional data (area under the ROC curve of 0.815) significantly outperforms detection based on either of the individual modalities (0.704 (T2-w) and 0.682 (DCE)). It was also found that a meta-classifier trained directly on integrated T2-w and DCE data (data-level integration) significantly outperformed a decision-level meta-classifier, constructed by combining the classifier outputs from the individual T2-w and DCE channels.
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