Institute for Community Medicine, Ernst Moritz Arndt University of Greifswald, Greifswald, Germany.
Phys Med Biol. 2013 Sep 7;58(17):5899-915. doi: 10.1088/0031-9155/58/17/5899. Epub 2013 Aug 6.
The whole prostatic volume (PV) is an important indicator for benign prostate hyperplasia. Correlating the PV with other clinical parameters in a population-based prospective cohort study (SHIP-2) requires valid prostate segmentation in a large number of whole-body MRI scans. The axial proton density fast spin echo fat saturated sequence is used for prostate screening in SHIP-2. Our automated segmentation method is based on support vector machines (SVM). We used three-dimensional neighborhood information to build classification vectors from automatically generated features and randomly selected 16 MR examinations for validation. The Hausdorff distance reached a mean value of 5.048 ± 2.413, and a mean value of 5.613 ± 2.897 compared to manual segmentation by observers A and B. The comparison between volume measurement of SVM-based segmentation and manual segmentation of observers A and B depicts a strong correlation resulting in Spearman's rank correlation coefficients (ρ) of 0.936 and 0.859, respectively. Our automated methodology based on SVM for prostate segmentation can segment the prostate in WBI scans with good segmentation quality and has considerable potential for integration in epidemiological studies.
前列腺总体积（PV）是良性前列腺增生的一个重要指标。在基于人群的前瞻性队列研究（SHIP-2）中，将 PV 与其他临床参数相关联需要在大量全身 MRI 扫描中对前列腺进行有效的分割。SHIP-2 中使用轴向质子密度快速自旋回波脂肪饱和序列进行前列腺筛查。我们的自动分割方法基于支持向量机（SVM）。我们使用三维邻域信息从自动生成的特征和随机选择的 16 次磁共振检查中构建分类向量，以进行验证。Hausdorff 距离的平均值达到了 5.048±2.413，与观察者 A 和 B 的手动分割相比，平均值分别为 5.613±2.897。SVM 分割的体积测量与观察者 A 和 B 的手动分割之间的比较显示出很强的相关性，导致 Spearman 秩相关系数（ρ）分别为 0.936 和 0.859。我们基于 SVM 的前列腺自动分割方法可以对 WBI 扫描中的前列腺进行分割，具有良好的分割质量，并且在流行病学研究中有很大的集成潜力。
