Computer-Aided Detection of Prostate Cancer inMRI
Computer-Aided Detection of Prostate Cancer inMRI
复制标题
DOI：
10.1109/tmi.2014.2303821
复制
发表时间：
2014-05-01
影响因子：
10.6
通讯作者：
Huisman, Henkjan
中科院分区：
文献类型：
--
作者：
Litjens, Geert;Debats, Oscar;Huisman, Henkjan
前列腺癌是西方世界男性癌症死亡的主要原因之一。磁共振成像(MRI)正越来越多地被用作检测前列腺癌的一种手段。因此，计算机辅助检测MRI图像中的前列腺癌已成为一个活跃的研究领域。本文研究了一个由两个阶段组成的全自动计算机辅助检测系统。在第一阶段，我们使用基于多图谱的前列腺分割、体素特征提取、分类和局部极大值检测来检测初始候选。第二阶段对候选区域进行分割，通过分类得到每个候选区域的癌症概率。特征代表药代动力学行为、对称性和外观等。该系统在347名患者的大的连续队列中进行了评估，并以MR引导的活检为参考标准。这组患者包括165名癌症患者和182名非前列腺癌患者。性能评估基于基于病变的自由反应接收器工作特性曲线和基于患者的接收器工作特性分析。该系统还与放射科医生的预期临床表现进行了比较。结果显示，在每个正常病例中，0.1、1和10个假阳性的敏感度分别为0.42、0.75和0.89。在临床工作流程中，该系统有可能用于提高放射科医生的敏感度。在筛查情况下典型的高特异性读数设置下，该系统的性能与放射科医生没有显著不同，可以用作独立的第二阅读器而不是第二放射科医生。此外，该系统在第一阅读器环境中具有潜力。
Prostate cancer is one of the major causes of cancer death for men in the western world. Magnetic resonance imaging (MRI) is being increasingly used as a modality to detect prostate cancer. Therefore, computer-aided detection of prostate cancer in MRI images has become an active area of research. In this paper we investigate a fully automated computer-aided detection system which consists of two stages. In the first stage, we detect initial candidates using multi-atlas-based prostate segmentation, voxel feature extraction, classification and local maxima detection. The second stage segments the candidate regions and using classification we obtain cancer likelihoods for each candidate. Features represent pharmacokinetic behavior, symmetry and appearance, among others. The system is evaluated on a large consecutive cohort of 347 patients with MR-guided biopsy as the reference standard. This set contained 165 patients with cancer and 182 patients without prostate cancer. Performance evaluation is based on lesion-based free-response receiver operating characteristic curve and patient-based receiver operating characteristic analysis. The system is also compared to the prospective clinical performance of radiologists. Results show a sensitivity of 0.42, 0.75, and 0.89 at 0.1, 1, and 10 false positives per normal case. In clinical workflow the system could potentially be used to improve the sensitivity of the radiologist. At the high specificity reading setting, which is typical in screening situations, the system does not perform significantly different from the radiologist and could be used as an independent second reader instead of a second radiologist. Furthermore, the system has potential in a first-reader setting.