CONFIDENCE OF MODEL BASED SHAPE RECONSTRUCTION FROM SPARSE DATA

被引:6
作者
Baka, N. [1 ]
de Bruijne, M. [1 ,2 ,3 ]
Reiber, J. H. C.
Niessen, W. [1 ,4 ]
Lelieveldt, B. P. F. [4 ]
机构
[1] Erasmus MC, Rotterdam, Netherlands
[2] Univ Copenhagen, Dept Comp Sci, Copenhagen, Denmark
[3] Univ Copenhagen, Dept Comp Sci, Copenhagen, Denmark
[4] Delft Univ Technol, Delft, Netherlands
来源
2010 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO | 2010年
关键词
SSM; PDM; sparse reconstruction; shape confidence; noisy data; point uncertainty;
D O I
10.1109/ISBI.2010.5490179
中图分类号
R318 [生物医学工程];
学科分类号
100103 [病原生物学];
摘要
Statistical shape models (SSM) are commonly applied for plausible interpolation of missing data in medical imaging. However, when fitting a shape model to sparse information, many solutions may fit the available data. In this paper we derive a constrained SSM to fit noisy sparse input landmarks and assign a confidence value to the resulting reconstructed shape. An evaluation study is performed to compare three methods used for sparse SSM fitting w.r.t. specificity, generalization ability, and correctness of estimated confidence limits with an increasing amount of input information. We find that the proposed constrained shape model outperforms the other models, is robust against the selection and amount of sparse information, and indicates the shape confidence well.
引用
收藏
页码:1077 / 1080
页数:4
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