García Molina José Fernando, Zheng Lei, Sertdemir Metin, Dinter Dietmar J, Schönberg Stefan, Rädle Matthias
Institute of Experimental Radiation Oncology, Department of Radiation Oncology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
PLoS One. 2014 Apr 3;9(4):e93600. doi: 10.1371/journal.pone.0093600. eCollection 2014.
Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. The proposed system integrates anatomic, texture, and functional features. The data set was preprocessed using B-Spline interpolation, bias field correction and intensity standardization. First- and second-order angular independent statistical approaches and rotation invariant local phase quantization (RI-LPQ) were utilized to quantify texture information. An incremental learning ensemble SVM was implemented to suit working conditions in medical applications and to improve effectiveness and robustness of the system. The probability estimation of cancer structures was calculated using SVM and the corresponding optimization was carried out with a heuristic method together with a 3-fold cross-validation methodology. We achieved an average sensitivity of 0.844 ± 0.068 and a specificity of 0.780 ± 0.038, which yielded superior or similar performance to current state of the art using a total database of only 41 slices from twelve patients with histological confirmed information, including cancerous, unhealthy non-cancerous and healthy prostate tissue. Our results show the feasibility of an ensemble SVM being able to learn additional information from new data while preserving previously acquired knowledge and preventing unlearning. The use of texture descriptors provides more salient discriminative patterns than the functional information used. Furthermore, the system improves selection of information, efficiency and robustness of the classification. The generated probability map enables radiologists to have a lower variability in diagnosis, decrease false negative rates and reduce the time to recognize and delineate structures in the prostate.
由于前列腺癌存在多种变体且在磁共振成像（MR）图像中有不同表现，因此对其进行可靠检测具有挑战性。我们提出了一种模式识别系统，该系统采用基于支持向量机（SVM）的增量学习集成算法，利用多模态MR图像和基于纹理的信息策略来解决这一问题。所提出的系统整合了解剖、纹理和功能特征。数据集经过B样条插值、偏置场校正和强度标准化预处理。利用一阶和二阶角度无关统计方法以及旋转不变局部相位量化（RI-LPQ）来量化纹理信息。实现了增量学习集成SVM，以适应医学应用中的工作条件，并提高系统的有效性和鲁棒性。使用SVM计算癌症结构的概率估计，并采用启发式方法和3折交叉验证方法进行相应优化。我们仅使用来自12名经组织学确诊的患者（包括癌性、不健康非癌性和健康前列腺组织）的41个切片的总数据库，就实现了平均灵敏度为0.844±0.068，特异性为0.780±0.038，其性能优于或类似于当前的先进技术。我们的结果表明，集成SVM能够从新数据中学习额外信息，同时保留先前获取的知识并防止遗忘。与所使用的功能信息相比，纹理描述符的使用提供了更显著的判别模式。此外，该系统提高了信息选择、分类效率和鲁棒性。生成的概率图使放射科医生在诊断中具有更低的变异性，降低假阴性率，并减少识别和勾勒前列腺结构的时间。
