Characterizing spatially varying performance to improve multi-atlas multi-label segmentation.
Characterizing spatially varying performance to improve multi-atlas multi-label segmentation.
复制标题
DOI:
10.1007/978-3-642-22092-0_8
复制
发表时间:
2011
期刊:
影响因子:
--
通讯作者:
Landman, Bennett A.
中科院分区:
文献类型:
--
作者:
Asman, Andrew J.;Landman, Bennett A.
关键词:
Segmentation of medical images has become critical to building understanding of biological structure-functional relationships. Atlas registration and label transfer provide a fully-automated approach for deriving segmentations given atlas training data. When multiple atlases are used, statistical label fusion techniques have been shown to dramatically improve segmentation accuracy. However, these techniques have had limited success with complex structures and atlases with varying similarity to the target data. Previous approaches have parameterized raters by a single confusion matrix, so that spatially varying performance for a single rater is neglected. Herein, we reformulate the statistical fusion model to describe raters by regional confusion matrices so that co-registered atlas labels can be fused in an optimal, spatially varying manner, which leads to an improved label fusion estimation with heterogeneous atlases. The advantages of this approach are characterized in a simulation and an empirical whole-brain labeling task.
登录
查看更多内容
影响因子:
10.6
作者:
Warfield, SK;Zou, KH;Wells, WM
通讯作者:
Wells, WM
影响因子:
10.6
作者:
Rohde, GK;Aldroubi, A;Dawant, BM
通讯作者:
Dawant, BM
影响因子:
4.8
作者:
DICE, LR
通讯作者:
DICE, LR
影响因子:
10.6
作者:
Isgum, Ivana;Staring, Marius;van Ginneken, Brain
通讯作者:
van Ginneken, Brain
DOI:
10.1002/jmri.1880020603
发表时间:
1992-11-01
期刊:
JMRI-JOURNAL OF MAGNETIC RESONANCE IMAGING
影响因子:
--
作者:
KIKINIS, R;SHENTON, ME;JOLESZ, FA
通讯作者:
JOLESZ, FA