Joint segmentation and registration for infant brain images

Guorong Wu, Li Wang, John Gilmore, Weili Lin, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately characterize structure changes is very critical in early brain development studies, which highly relies on the performance of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than the adult brains due to dynamic appearance change with rapid brain development. Fortunately, image segmentation and registration of infant images can assist each other to overcome the above difficulties by harnessing the growth trajectories (temporal correspondences) learned from a large set of training subjects with complete longitudinal data. To this end, we propose a joint segmentation and registration algorithm for infant brain images. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our joint segmentation and registration method in early brain development studies.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages13-21
Number of pages9
Volume8848
ISBN (Print)9783319139715
DOIs
Publication statusPublished - 2014 Jan 1
Externally publishedYes
EventInternational Workshop on Medical Computer Vision: Algorithms for Big Data was held in conjunction with 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-bigMCV 2014 - Cambridge, United States
Duration: 2014 Sep 182014 Sep 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8848
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherInternational Workshop on Medical Computer Vision: Algorithms for Big Data was held in conjunction with 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-bigMCV 2014
CountryUnited States
CityCambridge
Period14/9/1814/9/18

Fingerprint

Registration
Brain
Segmentation
Image registration
Image Registration
Image segmentation
Image Segmentation
Longitudinal Data
Large Set
Correspondence
Trajectories
Trajectory

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Wu, G., Wang, L., Gilmore, J., Lin, W., & Shen, D. (2014). Joint segmentation and registration for infant brain images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8848, pp. 13-21). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8848). Springer Verlag. https://doi.org/10.1007/978-3-319-13972-2_2

Joint segmentation and registration for infant brain images. / Wu, Guorong; Wang, Li; Gilmore, John; Lin, Weili; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8848 Springer Verlag, 2014. p. 13-21 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8848).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wu, G, Wang, L, Gilmore, J, Lin, W & Shen, D 2014, Joint segmentation and registration for infant brain images. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8848, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8848, Springer Verlag, pp. 13-21, International Workshop on Medical Computer Vision: Algorithms for Big Data was held in conjunction with 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-bigMCV 2014, Cambridge, United States, 14/9/18. https://doi.org/10.1007/978-3-319-13972-2_2
Wu G, Wang L, Gilmore J, Lin W, Shen D. Joint segmentation and registration for infant brain images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8848. Springer Verlag. 2014. p. 13-21. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-13972-2_2
Wu, Guorong ; Wang, Li ; Gilmore, John ; Lin, Weili ; Shen, Dinggang. / Joint segmentation and registration for infant brain images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8848 Springer Verlag, 2014. pp. 13-21 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{e588a00bd7bb4bc7971593852161bf01,
title = "Joint segmentation and registration for infant brain images",
abstract = "The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately characterize structure changes is very critical in early brain development studies, which highly relies on the performance of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than the adult brains due to dynamic appearance change with rapid brain development. Fortunately, image segmentation and registration of infant images can assist each other to overcome the above difficulties by harnessing the growth trajectories (temporal correspondences) learned from a large set of training subjects with complete longitudinal data. To this end, we propose a joint segmentation and registration algorithm for infant brain images. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our joint segmentation and registration method in early brain development studies.",
author = "Guorong Wu and Li Wang and John Gilmore and Weili Lin and Dinggang Shen",
year = "2014",
month = "1",
day = "1",
doi = "10.1007/978-3-319-13972-2_2",
language = "English",
isbn = "9783319139715",
volume = "8848",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "13--21",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Joint segmentation and registration for infant brain images

AU - Wu, Guorong

AU - Wang, Li

AU - Gilmore, John

AU - Lin, Weili

AU - Shen, Dinggang

PY - 2014/1/1

Y1 - 2014/1/1

N2 - The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately characterize structure changes is very critical in early brain development studies, which highly relies on the performance of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than the adult brains due to dynamic appearance change with rapid brain development. Fortunately, image segmentation and registration of infant images can assist each other to overcome the above difficulties by harnessing the growth trajectories (temporal correspondences) learned from a large set of training subjects with complete longitudinal data. To this end, we propose a joint segmentation and registration algorithm for infant brain images. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our joint segmentation and registration method in early brain development studies.

AB - The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately characterize structure changes is very critical in early brain development studies, which highly relies on the performance of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than the adult brains due to dynamic appearance change with rapid brain development. Fortunately, image segmentation and registration of infant images can assist each other to overcome the above difficulties by harnessing the growth trajectories (temporal correspondences) learned from a large set of training subjects with complete longitudinal data. To this end, we propose a joint segmentation and registration algorithm for infant brain images. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our joint segmentation and registration method in early brain development studies.

UR - http://www.scopus.com/inward/record.url?scp=84917739976&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84917739976&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-13972-2_2

DO - 10.1007/978-3-319-13972-2_2

M3 - Conference contribution

SN - 9783319139715

VL - 8848

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 13

EP - 21

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -