Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics

Guorong Wu, Qian Wang, Hongjun Jia, Dinggang Shen

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

Abstract

Accurate measurement of longitudinal changes of anatomical structure is important and challenging in many clinical studies. Also, for identification of disease-affected regions due to the brain disease, it is extremely necessary to register a population data to the common space simultaneously. In this paper, we propose a new method for simultaneous longitudinal and groupwise registration of a set of longitudinal data acquired from multiple subjects. Our goal is to 1) consistently measure the longitudinal changes from a sequence of longitudinal data acquired from the same subject; and 2) jointly align all image data (acquired from all time points of all subjects) to a hidden common space. To achieve these two goals, we first introduce a set of temporal fiber bundles to explore the spatial-temporal behavior of anatomical changes in each longitudinal data of the same subject. Then, a probabilistic model is built upon the hidden state of spatial smoothness and temporal continuity on the fibers. Finally, the transformation fields that connect each time-point image of each subject to the common space are simultaneously estimated by the expectation maximization (EM) approach, via the maximum a posterior (MAP) estimation of probabilistic models. Promising results are obtained to quantitatively measure the longitudinal changes of hippocampus volume, indicating better performance of our method than the conventional pairwise methods.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages618-625
Number of pages8
Volume6362 LNCS
EditionPART 2
DOIs
Publication statusPublished - 2010 Nov 22
Externally publishedYes
Event13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 - Beijing, China
Duration: 2010 Sep 202010 Sep 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6362 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
CountryChina
CityBeijing
Period10/9/2010/9/24

Fingerprint

Image Sequence
Registration
Template
Longitudinal Data
Heuristics
Fibers
Probabilistic Model
Brain
Hippocampus
Expectation Maximization
Fiber Bundle
Pairwise
Smoothness
Fiber
Necessary
Statistical Models

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Wu, G., Wang, Q., Jia, H., & Shen, D. (2010). Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 6362 LNCS, pp. 618-625). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6362 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-15745-5_76

Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics. / Wu, Guorong; Wang, Qian; Jia, Hongjun; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6362 LNCS PART 2. ed. 2010. p. 618-625 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6362 LNCS, No. PART 2).

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

Wu, G, Wang, Q, Jia, H & Shen, D 2010, Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 6362 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6362 LNCS, pp. 618-625, 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010, Beijing, China, 10/9/20. https://doi.org/10.1007/978-3-642-15745-5_76
Wu G, Wang Q, Jia H, Shen D. Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 6362 LNCS. 2010. p. 618-625. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-15745-5_76
Wu, Guorong ; Wang, Qian ; Jia, Hongjun ; Shen, Dinggang. / Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6362 LNCS PART 2. ed. 2010. pp. 618-625 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
@inproceedings{078be9202d144805b58884153088d24b,
title = "Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics",
abstract = "Accurate measurement of longitudinal changes of anatomical structure is important and challenging in many clinical studies. Also, for identification of disease-affected regions due to the brain disease, it is extremely necessary to register a population data to the common space simultaneously. In this paper, we propose a new method for simultaneous longitudinal and groupwise registration of a set of longitudinal data acquired from multiple subjects. Our goal is to 1) consistently measure the longitudinal changes from a sequence of longitudinal data acquired from the same subject; and 2) jointly align all image data (acquired from all time points of all subjects) to a hidden common space. To achieve these two goals, we first introduce a set of temporal fiber bundles to explore the spatial-temporal behavior of anatomical changes in each longitudinal data of the same subject. Then, a probabilistic model is built upon the hidden state of spatial smoothness and temporal continuity on the fibers. Finally, the transformation fields that connect each time-point image of each subject to the common space are simultaneously estimated by the expectation maximization (EM) approach, via the maximum a posterior (MAP) estimation of probabilistic models. Promising results are obtained to quantitatively measure the longitudinal changes of hippocampus volume, indicating better performance of our method than the conventional pairwise methods.",
author = "Guorong Wu and Qian Wang and Hongjun Jia and Dinggang Shen",
year = "2010",
month = "11",
day = "22",
doi = "10.1007/978-3-642-15745-5_76",
language = "English",
isbn = "3642157440",
volume = "6362 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "618--625",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 2",

}

TY - GEN

T1 - Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics

AU - Wu, Guorong

AU - Wang, Qian

AU - Jia, Hongjun

AU - Shen, Dinggang

PY - 2010/11/22

Y1 - 2010/11/22

N2 - Accurate measurement of longitudinal changes of anatomical structure is important and challenging in many clinical studies. Also, for identification of disease-affected regions due to the brain disease, it is extremely necessary to register a population data to the common space simultaneously. In this paper, we propose a new method for simultaneous longitudinal and groupwise registration of a set of longitudinal data acquired from multiple subjects. Our goal is to 1) consistently measure the longitudinal changes from a sequence of longitudinal data acquired from the same subject; and 2) jointly align all image data (acquired from all time points of all subjects) to a hidden common space. To achieve these two goals, we first introduce a set of temporal fiber bundles to explore the spatial-temporal behavior of anatomical changes in each longitudinal data of the same subject. Then, a probabilistic model is built upon the hidden state of spatial smoothness and temporal continuity on the fibers. Finally, the transformation fields that connect each time-point image of each subject to the common space are simultaneously estimated by the expectation maximization (EM) approach, via the maximum a posterior (MAP) estimation of probabilistic models. Promising results are obtained to quantitatively measure the longitudinal changes of hippocampus volume, indicating better performance of our method than the conventional pairwise methods.

AB - Accurate measurement of longitudinal changes of anatomical structure is important and challenging in many clinical studies. Also, for identification of disease-affected regions due to the brain disease, it is extremely necessary to register a population data to the common space simultaneously. In this paper, we propose a new method for simultaneous longitudinal and groupwise registration of a set of longitudinal data acquired from multiple subjects. Our goal is to 1) consistently measure the longitudinal changes from a sequence of longitudinal data acquired from the same subject; and 2) jointly align all image data (acquired from all time points of all subjects) to a hidden common space. To achieve these two goals, we first introduce a set of temporal fiber bundles to explore the spatial-temporal behavior of anatomical changes in each longitudinal data of the same subject. Then, a probabilistic model is built upon the hidden state of spatial smoothness and temporal continuity on the fibers. Finally, the transformation fields that connect each time-point image of each subject to the common space are simultaneously estimated by the expectation maximization (EM) approach, via the maximum a posterior (MAP) estimation of probabilistic models. Promising results are obtained to quantitatively measure the longitudinal changes of hippocampus volume, indicating better performance of our method than the conventional pairwise methods.

UR - http://www.scopus.com/inward/record.url?scp=78349299413&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78349299413&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-15745-5_76

DO - 10.1007/978-3-642-15745-5_76

M3 - Conference contribution

SN - 3642157440

SN - 9783642157448

VL - 6362 LNCS

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

SP - 618

EP - 625

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

ER -