Ou Yangming, Ye Dong Hye, Pohl Kilian M, Davatzikos Christos
Section of Biomedical Image Analysis (SBIA), Department of Radiology, University of Pennsylvania.
Biomed Image Regist Proc. 2012 Jul;7359:209-219. doi: 10.1007/978-3-642-31340-0_22.
Cross-subject image registration is the building block for many cardiac studies. In the literature, it is often handled by voxel-wise registration methods. However, studies are lacking to show which methods are more accurate and stable in this context. Aiming at answering this question, this paper evaluates 12 popular registration methods and validates a recently developed method DRAMMS [16] in the context of cross-subject cardiac registration. Our dataset consists of short-axis end-diastole cardiac MR images from 24 subjects, in which non-cardiac structures are removed. Each registration method was applied to all 552 image pairs. Registration accuracy is approximated by Jaccard overlap between deformed expert annotation of source image and the corresponding expert annotation of target image. This accuracy surrogate is further correlated with deformation aggressiveness, which is reflected by minimum, maximum and range of Jacobian determinants. Our study shows that DRAMMS [16] scores high in accuracy and well balances accuracy and aggressiveness in this dataset, followed by ANTs [13], MI-FFD [14], Demons [15], and ART [12]. Our findings in cross-subject cardiac registrations echo those findings in brain image registrations [7].
跨对象图像配准是许多心脏研究的基础。在文献中，它通常由基于体素的配准方法来处理。然而，缺乏研究表明在这种情况下哪些方法更准确、更稳定。为了回答这个问题，本文评估了12种流行的配准方法，并在跨对象心脏配准的背景下验证了一种最近开发的方法DRAMMS [16]。我们的数据集由24名受试者的短轴舒张末期心脏磁共振图像组成，其中非心脏结构已被去除。每种配准方法都应用于所有552对图像。配准精度通过源图像的变形专家注释与目标图像的相应专家注释之间的杰卡德重叠来近似。这种精度替代指标进一步与变形的剧烈程度相关，变形的剧烈程度由雅可比行列式的最小值、最大值和范围来反映。我们的研究表明，DRAMMS [16]在精度方面得分很高，并且在该数据集中很好地平衡了精度和剧烈程度，其次是ANTS [13]、MI-FFD [14]、Demons [15]和ART [12]。我们在跨对象心脏配准中的发现与脑图像配准中的那些发现[7]相呼应。
