van Ginneken Bram, Stegmann Mikkel B, Loog Marco
Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
Med Image Anal. 2006 Feb;10(1):19-40. doi: 10.1016/j.media.2005.02.002.
The task of segmenting the lung fields, the heart, and the clavicles in standard posterior-anterior chest radiographs is considered. Three supervised segmentation methods are compared: active shape models, active appearance models and a multi-resolution pixel classification method that employs a multi-scale filter bank of Gaussian derivatives and a k-nearest-neighbors classifier. The methods have been tested on a publicly available database of 247 chest radiographs, in which all objects have been manually segmented by two human observers. A parameter optimization for active shape models is presented, and it is shown that this optimization improves performance significantly. It is demonstrated that the standard active appearance model scheme performs poorly, but large improvements can be obtained by including areas outside the objects into the model. For lung field segmentation, all methods perform well, with pixel classification giving the best results: a paired t-test showed no significant performance difference between pixel classification and an independent human observer. For heart segmentation, all methods perform comparably, but significantly worse than a human observer. Clavicle segmentation is a hard problem for all methods; best results are obtained with active shape models, but human performance is substantially better. In addition, several hybrid systems are investigated. For heart segmentation, where the separate systems perform comparably, significantly better performance can be obtained by combining the results with majority voting. As an application, the cardio-thoracic ratio is computed automatically from the segmentation results. Bland and Altman plots indicate that all methods perform well when compared to the gold standard, with confidence intervals from pixel classification and active appearance modeling very close to those of a human observer. All results, including the manual segmentations, have been made publicly available to facilitate future comparative studies.
本文考虑了在标准后前位胸片中对肺野、心脏和锁骨进行分割的任务。比较了三种监督分割方法：主动形状模型、主动外观模型和一种多分辨率像素分类方法，该方法采用高斯导数的多尺度滤波器组和k近邻分类器。这些方法在一个包含247张胸片的公开数据库上进行了测试，其中所有对象均由两名人类观察者手动分割。提出了主动形状模型的参数优化方法，并表明这种优化显著提高了性能。结果表明，标准的主动外观模型方案性能较差，但通过将对象外部的区域纳入模型可以得到很大改进。对于肺野分割，所有方法都表现良好，像素分类给出了最佳结果：配对t检验表明像素分类与独立的人类观察者之间在性能上没有显著差异。对于心脏分割，所有方法的表现相当，但明显比人类观察者差。锁骨分割对所有方法来说都是一个难题；主动形状模型取得了最好的结果，但人类的表现要好得多。此外，还研究了几种混合系统。对于心脏分割，单独的系统表现相当，通过多数投票组合结果可以显著提高性能。作为一个应用，从分割结果中自动计算心胸比率。布兰德-奥特曼图表明，与金标准相比，所有方法都表现良好，像素分类和主动外观建模的置信区间与人类观察者的非常接近。所有结果，包括手动分割结果，都已公开提供，以方便未来的比较研究。
