Confidence of model based shape reconstruction from sparse data

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Confidence of model based shape reconstruction from sparse data. / Baka, N.; de Bruijne, Marleen; Reiber, J. H. C.; Niessen, Wiro; Lelieveldt, B. P. F.

2010 IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE, 2010. p. 1077-1080.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Baka, N, de Bruijne, M, Reiber, JHC, Niessen, W & Lelieveldt, BPF 2010, Confidence of model based shape reconstruction from sparse data. in 2010 IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE, pp. 1077-1080, 7th IEEE International Symposium on Biomedical Imaging, Rotterdam, Netherlands, 14/04/2010. https://doi.org/10.1109/ISBI.2010.5490179

APA

Baka, N., de Bruijne, M., Reiber, J. H. C., Niessen, W., & Lelieveldt, B. P. F. (2010). Confidence of model based shape reconstruction from sparse data. In 2010 IEEE International Symposium on Biomedical Imaging: from nano to macro (pp. 1077-1080). IEEE. https://doi.org/10.1109/ISBI.2010.5490179

Vancouver

Baka N, de Bruijne M, Reiber JHC, Niessen W, Lelieveldt BPF. Confidence of model based shape reconstruction from sparse data. In 2010 IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE. 2010. p. 1077-1080 https://doi.org/10.1109/ISBI.2010.5490179

Author

Baka, N. ; de Bruijne, Marleen ; Reiber, J. H. C. ; Niessen, Wiro ; Lelieveldt, B. P. F. / Confidence of model based shape reconstruction from sparse data. 2010 IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE, 2010. pp. 1077-1080

Bibtex

@inproceedings{a396c1b043fc11df928f000ea68e967b,
title = "Confidence of model based shape reconstruction from sparse data",
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.",
author = "N. Baka and {de Bruijne}, Marleen and Reiber, {J. H. C.} and Wiro Niessen and Lelieveldt, {B. P. F.}",
year = "2010",
doi = "10.1109/ISBI.2010.5490179",
language = "English",
isbn = "978-1-4244-4125-9",
pages = "1077--1080",
booktitle = "2010 IEEE International Symposium on Biomedical Imaging",
publisher = "IEEE",
note = "7th IEEE International Symposium on Biomedical Imaging : from nano to macro, ISBI 2010 ; Conference date: 14-04-2010 Through 17-04-2010",

}

RIS

TY - GEN

T1 - Confidence of model based shape reconstruction from sparse data

AU - Baka, N.

AU - de Bruijne, Marleen

AU - Reiber, J. H. C.

AU - Niessen, Wiro

AU - Lelieveldt, B. P. F.

N1 - Conference code: 7

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

U2 - 10.1109/ISBI.2010.5490179

DO - 10.1109/ISBI.2010.5490179

M3 - Article in proceedings

SN - 978-1-4244-4125-9

SP - 1077

EP - 1080

BT - 2010 IEEE International Symposium on Biomedical Imaging

PB - IEEE

T2 - 7th IEEE International Symposium on Biomedical Imaging

Y2 - 14 April 2010 through 17 April 2010

ER -

ID: 19119936