Huang Adam, Li Jiang, Summers Ronald M, Petrick Nicholas, Hara Amy K
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892-1182.
Pattern Recognit Lett. 2010 Mar 21;31(11):1461-1469. doi: 10.1016/j.patrec.2010.03.013.
We investigated a Pareto front approach to improving polyp detection algorithms for CT colonography (CTC). A dataset of 56 CTC colon surfaces with 87 proven positive detections of 53 polyps sized 4 to 60 mm was used to evaluate the performance of a one-step and a two-step curvature-based region growing algorithm. The algorithmic performance was statistically evaluated and compared based on the Pareto optimal solutions from 20 experiments by evolutionary algorithms. The false positive rate was lower (p<0.05) by the two-step algorithm than by the one-step for 63% of all possible operating points. While operating at a suitable sensitivity level such as 90.8% (79/87) or 88.5% (77/87), the false positive rate was reduced by 24.4% (95% confidence intervals 17.9-31.0%) or 45.8% (95% confidence intervals 40.1-51.0%) respectively. We demonstrated that, with a proper experimental design, the Pareto optimization process can effectively help in fine-tuning and redesigning polyp detection algorithms.
我们研究了一种用于改进CT结肠成像（CTC）息肉检测算法的帕累托前沿方法。使用一个包含56个CTC结肠表面的数据集，其中有87个经证实的阳性检测结果，涉及53个大小为4至60毫米的息肉，以评估基于曲率的一步法和两步法区域生长算法的性能。基于进化算法20次实验得到的帕累托最优解，对算法性能进行了统计评估和比较。在所有可能的操作点中，63%的情况下，两步法的假阳性率低于一步法（p<0.05）。当在合适的灵敏度水平下运行，如90.8%（79/87）或88.5%（77/87）时，假阳性率分别降低了24.4%（95%置信区间17.9 - 31.0%）或45.8%（95%置信区间40.1 - 51.0%）。我们证明，通过适当的实验设计，帕累托优化过程可以有效地帮助微调及重新设计息肉检测算法。
