-
Barbara André,
Tom Vercauteren,
Anna M. Buchner,
Michael B. Wallace,
and Nicholas Ayache.
Learning Semantic and Visual Similarity for Endomicroscopy Video Retrieval.
IEEE Transactions on Medical Imaging,
2012.
Note: To appear.
HAL ID: inria-00618057.
@ARTICLE{andre:tmi:2012,
AUTHOR = {Barbara Andr\'e and Tom Vercauteren and Anna M. Buchner and Michael B. Wallace and Nicholas Ayache},
JOURNAL = {IEEE Transactions on Medical Imaging},
TITLE = {Learning Semantic and Visual Similarity for Endomicroscopy Video Retrieval},
YEAR = 2012,
X-EDITORIAL-BOARD ={yes},
X-INTERNATIONAL-AUDIENCE ={yes},
NOTE = {To appear},
URL = {http://www.inria.fr/sophia/asclepios/Publications/Barbara.Andre/ANDRE-TMI12-Manuscript.pdf},
URL-HAL = {http://hal.inria.fr/inria-00618057/en/},
HAL-IDENTIFIANT ={inria-00618057}
}
-
Stanley Durrleman,
Xavier Pennec,
Alain Trouvé,
Nicholas Ayache,
and José Braga.
Comparison of the endocranial ontogenies between chimpanzees and bonobos via temporal regression and spatiotemporal registration.
Journal of Human Evolution,
62(1):74 - 88,
2012.
ISSN: 0047-2484.
Keyword(s): Endocranium,
Virtual anthropology,
Ontogeny,
Shape regression,
Spatiotemporal registration.
@article{Durrleman:JHE:12,
title = "Comparison of the endocranial ontogenies between chimpanzees and bonobos via temporal regression and spatiotemporal registration",
journal = "Journal of Human Evolution",
volume = 62,
number = 1,
pages = "74 - 88",
year = 2012,
issn = "0047-2484",
doi = "10.1016/j.jhevol.2011.10.004",
url = "http://www.inria.fr/sophia/asclepios/Publications/Stanley.Durrleman/Durrleman_JHE_2012.pdf",
author = "Stanley Durrleman and Xavier Pennec and Alain Trouv\'e and Nicholas Ayache and Jos\'e Braga",
keywords = "Endocranium",
keywords = "Virtual anthropology",
keywords = "Ontogeny",
keywords = "Shape regression",
keywords = "Spatiotemporal registration",
X-EDITORIAL-BOARD ={yes},
X-INTERNATIONAL-AUDIENCE ={yes},
}
-
Christophe Person,
Valerie Louis-Dorr,
Sylvain Poussier,
Olivier Commowick,
Gregoire Malandain,
Louis Maillard,
Didier Wolf,
Nicolas Gillet,
Veronique Roch,
Gilles Karcher,
and Pierre-Yves Marie.
Voxel-Based Quantitative Analysis of Brain Images From 18F-FDG PET With a Block-Matching Algorithm for Spatial Normalization.
Clin Nucl Med,
37(3):268-73,
March 2012.
Abstract: |
OBJECTIVE: : Statistical Parametric Mapping (SPM) is widely used for the quantitative analysis of brain images from F fluorodeoxyglucose positron emission tomography (FDG PET). SPM requires an initial step of spatial normalization to align all images to a standard anatomic model (the template), but this may lead to image distortion and artifacts, especially in cases of marked brain abnormalities. This study aimed at assessing a block-matching (BM) normalization algorithm, where most transformations are not directly computed on the overall brain volume but through small blocks, a principle that is likely to minimize artifacts. METHODS: : Large and/or small hypometabolic areas were artificially simulated in initially normal FDG PET images to compare the results provided by statistical tests computed after either SPM or BM normalization. RESULTS: : Results were enhanced by BM, compared with SPM, with regard to (i) errors in the estimation of large defects volumes (about 2-fold lower) because of a lower image distortion, and (ii) rates of false-positive foci when numerous or extended abnormalities were simulated. These observations were strengthened by analyses of FDG PET examinations from epileptic patients. CONCLUSIONS: : Results obtained with the BM normalization of brain FDG PET appear more precise and robust than with SPM normalization, especially in cases of numerous or extended abnormalities. |
@ARTICLE{person:cnm:2012,
AUTHOR = {Christophe Person and Valerie Louis-Dorr and Sylvain Poussier and Olivier Commowick and Gregoire Malandain and Louis Maillard and Didier Wolf and Nicolas Gillet and Veronique Roch and Gilles Karcher and Pierre-Yves Marie},
JOURNAL = {Clin Nucl Med},
MONTH = {March},
NUMBER = 3,
PAGES = {268-73},
TITLE = {Voxel-Based Quantitative Analysis of Brain Images From 18F-FDG PET With a Block-Matching Algorithm for Spatial Normalization},
VOLUME = 37,
YEAR = 2012,
ABSTRACT = {OBJECTIVE: : Statistical Parametric Mapping (SPM) is widely used for the quantitative analysis of brain images from F fluorodeoxyglucose positron emission tomography (FDG PET). SPM requires an initial step of spatial normalization to align all images to a standard anatomic model (the template), but this may lead to image distortion and artifacts, especially in cases of marked brain abnormalities. This study aimed at assessing a block-matching (BM) normalization algorithm, where most transformations are not directly computed on the overall brain volume but through small blocks, a principle that is likely to minimize artifacts. METHODS: : Large and/or small hypometabolic areas were artificially simulated in initially normal FDG PET images to compare the results provided by statistical tests computed after either SPM or BM normalization. RESULTS: : Results were enhanced by BM, compared with SPM, with regard to (i) errors in the estimation of large defects volumes (about 2-fold lower) because of a lower image distortion, and (ii) rates of false-positive foci when numerous or extended abnormalities were simulated. These observations were strengthened by analyses of FDG PET examinations from epileptic patients. CONCLUSIONS: : Results obtained with the BM normalization of brain FDG PET appear more precise and robust than with SPM normalization, especially in cases of numerous or extended abnormalities.},
DOI = {10.1097/RLU.0b013e3182443b2d},
PMID = 22310254
}
-
M. Sermesant,
R. Chabiniok,
P. Chinchapatnam,
T. Mansi,
F. Billet,
P. Moireau,
J.M. Peyrat,
K. Wong,
J. Relan,
K. Rhode,
M. Ginks,
P. Lambiase,
H. Delingette,
M. Sorine,
C.A. Rinaldi,
D. Chapelle,
R. Razavi,
and N. Ayache.
Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: A preliminary clinical validation.
Medical Image Analysis,
16(1):201-215,
2012.
HAL ID: inria-00616191.
@ARTICLE{Sermesant:MEDIA:2012,
AUTHOR = {M. Sermesant and R. Chabiniok and P. Chinchapatnam and T. Mansi and F. Billet and P. Moireau and J.M. Peyrat and K. Wong and J. Relan and K. Rhode and M. Ginks and P. Lambiase and H. Delingette and M. Sorine and C.A. Rinaldi and D. Chapelle and R. Razavi and N. Ayache},
JOURNAL = {Medical Image Analysis},
TITLE = {Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in {CRT}: A preliminary clinical validation},
X-EDITORIAL-BOARD ={yes},
X-INTERNATIONAL-AUDIENCE ={yes},
YEAR = 2012,
Volume = 16,
NUMBER = 1,
PAGES = {201-215},
URL = {http://www.inria.fr/sophia/asclepios/Publications/Maxime.Sermesant/MedIAsermesant2011.pdf},
URL-HAL = {http://hal.inria.fr/inria-00616191/en/},
HAL-IDENTIFIANT ={inria-00616191}
}
-
Stefan Sommer,
Mads Nielsen,
and Xavier Pennec.
Sparsity and Scale: Compact Representations of Deformation for Diffeomorphic Registration.
In IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2012),
Breckenridge, Colorado, USA,
January 2012.
HAL ID: hal-00641357.
Abstract: |
In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation at the relevant scales only. With the LDDMM registration framework, enforcing sparsity results in compact representations but with limited ability to represent deformation across scales. In contrast, the LDDKBM extension of LDDMM allows representations of deformation at multiple scales but it does not favour compactness and hence may represent deformation at more scales than necessary. In this paper, we combine a sparsity prior with the multi-scale framework resulting in an algorithm allowing compact representation of deformation across scales. We present a mathematical formulation of the algorithm and evaluate it on a dataset of annotated lung CT images. |
@inproceedings{Sommer:MMBIA:2012,
hal_id = {hal-00641357},
url = {http://hal.inria.fr/hal-00641357/en/},
title = {{Sparsity and Scale: Compact Representations of Deformation for Diffeomorphic Registration}},
author = {Sommer, Stefan and Nielsen, Mads and Pennec, Xavier},
abstract = {{In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation at the relevant scales only. With the LDDMM registration framework, enforcing sparsity results in compact representations but with limited ability to represent deformation across scales. In contrast, the LDDKBM extension of LDDMM allows representations of deformation at multiple scales but it does not favour compactness and hence may represent deformation at more scales than necessary. In this paper, we combine a sparsity prior with the multi-scale framework resulting in an algorithm allowing compact representation of deformation across scales. We present a mathematical formulation of the algorithm and evaluate it on a dataset of annotated lung CT images.}},
x-language = {Anglais},
affiliation = {Department of computer Science [Copenhagen] - DIKU - University of Copenhagen - ASCLEPIOS - INRIA Sophia Antipolis - INRIA},
booktitle = {{IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2012)}},
address = {Breckenridge, Colorado, USA},
X-INTERNATIONAL-AUDIENCE ={yes},
year = 2012,
month = Jan,
pdf = {http://hal.inria.fr/hal-00641357/PDF/Sommer.mmbia12.pdf},
URL-HAL = {http://hal.inria.fr/hal-00641357/en/},
HAL-IDENTIFIANT ={hal-00641357},
X-EDITORIAL-BOARD ={yes},
X-PAYS = {DK},
X-PROCEEDINGS ={yes}
}