Reda Islam, Shalaby Ahmed, Elmogy Mohammed, Elfotouh Ahmed Abou, Khalifa Fahmi, El-Ghar Mohamed Abou, Hosseini-Asl Ehsan, Gimel'farb Georgy, Werghi Naoufel, El-Baz Ayman
Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; Bioengineering Department, University of Louisville, Louisville KY 40292, USA.
Bioengineering Department, University of Louisville, Louisville KY 40292, USA.
Comput Biol Med. 2017 Feb 1;81:148-158. doi: 10.1016/j.compbiomed.2016.12.010. Epub 2016 Dec 23.
Early detection of prostate cancer increases chances of patients' survival. Our automated non-invasive system for computer-aided diagnosis (CAD) of prostate cancer segments the prostate on diffusion-weighted magnetic resonance images (DW-MRI) acquired at different b-values, estimates its apparent diffusion coefficients (ADC), and classifies their descriptors - empirical cumulative distribution functions (CDF) - with a trained deep learning network. To segment the prostate, an evolving geometric (level-set-based) deformable model is guided by a speed function depending on intensity attributes extracted from the DW-MRI with nonnegative matrix factorization (NMF). For a more robust evolution, the attributes are fused with a probabilistic shape prior and estimated spatial dependencies between prostate voxels. To preserve continuity, the ADCs of the segmented prostate volume at different b-values are normalized and refined using a generalized Gauss-Markov random field image model. The CDFs of the refined ADCs at different b-values are considered global water diffusion features and used to distinguish between benign and malignant prostates. A deep learning network of stacked non-negativity-constrained auto-encoders (SNCAE) is trained to classify the benign or malignant prostates on the basis of the constructed CDFs. Our experiments on 53 clinical DW-MRI data sets resulted in 92.3% accuracy, 83.3% sensitivity, and 100% specificity, indicating that the proposed CAD system could be used as a reliable non-invasive diagnostic tool.
前列腺癌的早期检测可提高患者的生存率。我们用于前列腺癌计算机辅助诊断（CAD）的自动化非侵入性系统，能在不同b值下采集的扩散加权磁共振图像（DW-MRI）上对前列腺进行分割，估计其表观扩散系数（ADC），并使用经过训练的深度学习网络对其描述符——经验累积分布函数（CDF）进行分类。为了分割前列腺，一个基于水平集的演化几何可变形模型由一个速度函数引导，该速度函数取决于通过非负矩阵分解（NMF）从DW-MRI中提取的强度属性。为了实现更稳健的演化，这些属性与概率形状先验以及前列腺体素之间估计的空间依赖性相融合。为了保持连续性，使用广义高斯 - 马尔可夫随机场图像模型对不同b值下分割出的前列腺体积的ADC进行归一化和细化。不同b值下细化后的ADC的CDF被视为全局水扩散特征，并用于区分良性和恶性前列腺。训练了一个由堆叠的非负约束自动编码器（SNCAE）组成的深度学习网络，以根据构建的CDF对良性或恶性前列腺进行分类。我们对53个临床DW-MRI数据集进行的实验，准确率达到92.3%，灵敏度为83.3%，特异性为100%，这表明所提出的CAD系统可作为一种可靠的非侵入性诊断工具。
