Confidence of model based shape reconstruction from sparse data

N. Baka*, M. De Bruijne, J. H.C. Reiber, W. Niessen, B. P.F. Lelieveldt

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2010 7th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2010 - Proceedings
Pages1077-1080
Number of pages4
DOIs
Publication statusPublished - 2010
Event7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Rotterdam, Netherlands
Duration: 14 Apr 201017 Apr 2010

Publication series

Series2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings

Conference

Conference7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
Country/TerritoryNetherlands
CityRotterdam
Period14/04/1017/04/10

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