Learning appearance and shape evolution for infant image registration in the first year of life

Lifang Wei, Shunbo Hu, Yaozong Gao, Xiaohuan Cao, Guorong Wu, Dinggang Shen

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

3 Citations (Scopus)

Abstract

Quantify dynamic structural changes in the first year of life is a key step in early brain development studies, which is indispensable to accurate deformable image registration. However, very few registration methods can work universally well for infant brain images at arbitrary development stages from birth to one year old, mainly due to (1) large anatomical variations and (2) dynamic appearance changes. In this paper, we propose a novel learningbased registration method to not only align the anatomical structures but also estimate the appearance difference between two infant MR images with possible large age gap. To achieve this goal, we leverage the random forest regression and auto-context model to learn the evolution of shape and appearance from a set of longitudinal infant images (with subject-specific temporal correspondences well determined) and then predict both the deformation pathway and appearance change between two new infant subjects. After that, it becomes much easier to deploy any conventional image registration method to complete the remaining registration since the above challenges for current state-of-the-art registration methods have been solved successfully. We apply our proposed registration method to align infant brain images of different subjects from 2-week-old to 12-month-old. Promising registration results have been achieved in terms of registration accuracy, compared to the counterpart registration methods.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings
PublisherSpringer Verlag
Pages36-44
Number of pages9
Volume10019 LNCS
ISBN (Print)9783319471563
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 2016 Oct 172016 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10019 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period16/10/1716/10/17

Fingerprint

Image registration
Image Registration
Registration
Brain
Structural dynamics
Learning
Life
Random Forest
Structural Change
Leverage
Pathway
Quantify
Correspondence
Regression
Predict
Arbitrary
Estimate

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Wei, L., Hu, S., Gao, Y., Cao, X., Wu, G., & Shen, D. (2016). Learning appearance and shape evolution for infant image registration in the first year of life. In Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings (Vol. 10019 LNCS, pp. 36-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10019 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-47157-0_5

Learning appearance and shape evolution for infant image registration in the first year of life. / Wei, Lifang; Hu, Shunbo; Gao, Yaozong; Cao, Xiaohuan; Wu, Guorong; Shen, Dinggang.

Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 10019 LNCS Springer Verlag, 2016. p. 36-44 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10019 LNCS).

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

Wei, L, Hu, S, Gao, Y, Cao, X, Wu, G & Shen, D 2016, Learning appearance and shape evolution for infant image registration in the first year of life. in Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings. vol. 10019 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10019 LNCS, Springer Verlag, pp. 36-44, 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 16/10/17. https://doi.org/10.1007/978-3-319-47157-0_5
Wei L, Hu S, Gao Y, Cao X, Wu G, Shen D. Learning appearance and shape evolution for infant image registration in the first year of life. In Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 10019 LNCS. Springer Verlag. 2016. p. 36-44. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-47157-0_5
Wei, Lifang ; Hu, Shunbo ; Gao, Yaozong ; Cao, Xiaohuan ; Wu, Guorong ; Shen, Dinggang. / Learning appearance and shape evolution for infant image registration in the first year of life. Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 10019 LNCS Springer Verlag, 2016. pp. 36-44 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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