Rohlfing T, Maurer C R
Neuroscience Program, SRI International, Menlo Park, CA, USA.
Med Image Comput Comput Assist Interv. 2005;8(Pt 2):838-45. doi: 10.1007/11566489_103.
Combination of multiple segmentations has recently been introduced as an effective method to obtain segmentations that are more accurate than any of the individual input segmentations. This paper introduces a new way to combine multiple segmentations using a novel shape-based averaging method. Individual segmentations are combined based on the signed Euclidean distance maps of the labels in each input segmentation. Compared to label voting, the new combination method produces smoother, more regular output segmentations and avoids fragmentation of contiguous structures. Using publicly available segmented human brain MR images (IBSR database), we perform a quantitative comparison between shape-based averaging and label voting by combining random segmentations with controlled error magnitudes and known ground truth. Shape-based averaging generated combined segmentations that were closer to the ground truth than combinations from label voting for all numbers of input segmentations (up to ten). The relative advantage of shape-based averaging over voting was larger for fewer input segmentations, and larger for greater deviations of the input segmentations from the ground truth. We conclude that shape-based averaging improves the accuracy of combined segmentations, in particular when only a few input segmentations are available and when the quality of the input segmentations is low.
最近，多分割组合被作为一种有效的方法引入，用于获得比任何单个输入分割都更准确的分割结果。本文介绍了一种使用基于形状的新颖平均方法来组合多分割的新方法。基于每个输入分割中标签的带符号欧几里得距离图来组合单个分割。与标签投票相比，新的组合方法产生更平滑、更规则的输出分割，并避免连续结构的碎片化。使用公开可用的分割人脑磁共振图像（IBSR数据库），我们通过将具有可控误差幅度和已知地面真值的随机分割进行组合，对基于形状的平均和标签投票进行了定量比较。对于所有数量的输入分割（最多十个），基于形状的平均生成的组合分割比标签投票生成的组合分割更接近地面真值。对于较少的输入分割，基于形状的平均相对于投票的相对优势更大，并且对于输入分割与地面真值的偏差越大，该优势也越大。我们得出结论，基于形状的平均提高了组合分割的准确性，特别是在只有少数输入分割可用且输入分割质量较低的情况下。
