Groupwise registration by hierarchical anatomical correspondence detection.

Guorong Wu, Qian Wang, Hongjun Jia, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

8 Citations (Scopus)

Abstract

We present a novel feature-based groupwise registration method to simultaneously warp the subjects towards the common space. Due to the complexity of the groupwise registration, we resort to decoupling it into two easy-to-solve tasks, i.e., alternatively establishing the robust correspondences across different subjects and interpolating the dense deformation fields based on the detected sparse correspondences. Specifically, several novel strategies are proposed in the correspondence detection step. First, attribute vector, instead of intensity only, is used as a morphological signature to guide the anatomical correspondence detection among all subjects. Second, we detect correspondence only on the driving voxels with distinctive attribute vectors for avoiding the ambiguity in detecting correspondences for non-distinctive voxels. Third, soft correspondence assignment (allowing for adaptive detection of multiple correspondences in each subject) is also presented to help establish reliable correspondences across all subjects, which is particularly necessary in the beginning of groupwise registration. Based on the sparse correspondences detected on the driving voxels of each subject, thin-plate splines (TPS) are then used to propagate the correspondences on the driving voxels to the entire brain image for estimating the dense transformation for each subject. By iteratively repeating correspondence detection and dense transformation estimation, all the subjects will be aligned onto a common space simultaneously. Our groupwise registration algorithm has been extensively evaluated by 18 elderly brains, 16 NIREP, and 40 LONI data. In all experiments, our algorithm achieves more robust and accurate registration results, compared to a groupwise registration method and a pairwise registration method, respectively.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages684-691
Number of pages8
Volume13
EditionPt 2
Publication statusPublished - 2010 Nov 18

Fingerprint

Brain

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Wu, G., Wang, Q., Jia, H., & Shen, D. (2010). Groupwise registration by hierarchical anatomical correspondence detection. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 2 ed., Vol. 13, pp. 684-691)

Groupwise registration by hierarchical anatomical correspondence detection. / Wu, Guorong; Wang, Qian; Jia, Hongjun; Shen, Dinggang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 13 Pt 2. ed. 2010. p. 684-691.

Research output: Chapter in Book/Report/Conference proceedingChapter

Wu, G, Wang, Q, Jia, H & Shen, D 2010, Groupwise registration by hierarchical anatomical correspondence detection. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 edn, vol. 13, pp. 684-691.
Wu G, Wang Q, Jia H, Shen D. Groupwise registration by hierarchical anatomical correspondence detection. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 ed. Vol. 13. 2010. p. 684-691
Wu, Guorong ; Wang, Qian ; Jia, Hongjun ; Shen, Dinggang. / Groupwise registration by hierarchical anatomical correspondence detection. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 13 Pt 2. ed. 2010. pp. 684-691
@inbook{6383bfc124bc46348362670619f853da,
title = "Groupwise registration by hierarchical anatomical correspondence detection.",
abstract = "We present a novel feature-based groupwise registration method to simultaneously warp the subjects towards the common space. Due to the complexity of the groupwise registration, we resort to decoupling it into two easy-to-solve tasks, i.e., alternatively establishing the robust correspondences across different subjects and interpolating the dense deformation fields based on the detected sparse correspondences. Specifically, several novel strategies are proposed in the correspondence detection step. First, attribute vector, instead of intensity only, is used as a morphological signature to guide the anatomical correspondence detection among all subjects. Second, we detect correspondence only on the driving voxels with distinctive attribute vectors for avoiding the ambiguity in detecting correspondences for non-distinctive voxels. Third, soft correspondence assignment (allowing for adaptive detection of multiple correspondences in each subject) is also presented to help establish reliable correspondences across all subjects, which is particularly necessary in the beginning of groupwise registration. Based on the sparse correspondences detected on the driving voxels of each subject, thin-plate splines (TPS) are then used to propagate the correspondences on the driving voxels to the entire brain image for estimating the dense transformation for each subject. By iteratively repeating correspondence detection and dense transformation estimation, all the subjects will be aligned onto a common space simultaneously. Our groupwise registration algorithm has been extensively evaluated by 18 elderly brains, 16 NIREP, and 40 LONI data. In all experiments, our algorithm achieves more robust and accurate registration results, compared to a groupwise registration method and a pairwise registration method, respectively.",
author = "Guorong Wu and Qian Wang and Hongjun Jia and Dinggang Shen",
year = "2010",
month = "11",
day = "18",
language = "English",
volume = "13",
pages = "684--691",
booktitle = "Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention",
edition = "Pt 2",

}

TY - CHAP

T1 - Groupwise registration by hierarchical anatomical correspondence detection.

AU - Wu, Guorong

AU - Wang, Qian

AU - Jia, Hongjun

AU - Shen, Dinggang

PY - 2010/11/18

Y1 - 2010/11/18

N2 - We present a novel feature-based groupwise registration method to simultaneously warp the subjects towards the common space. Due to the complexity of the groupwise registration, we resort to decoupling it into two easy-to-solve tasks, i.e., alternatively establishing the robust correspondences across different subjects and interpolating the dense deformation fields based on the detected sparse correspondences. Specifically, several novel strategies are proposed in the correspondence detection step. First, attribute vector, instead of intensity only, is used as a morphological signature to guide the anatomical correspondence detection among all subjects. Second, we detect correspondence only on the driving voxels with distinctive attribute vectors for avoiding the ambiguity in detecting correspondences for non-distinctive voxels. Third, soft correspondence assignment (allowing for adaptive detection of multiple correspondences in each subject) is also presented to help establish reliable correspondences across all subjects, which is particularly necessary in the beginning of groupwise registration. Based on the sparse correspondences detected on the driving voxels of each subject, thin-plate splines (TPS) are then used to propagate the correspondences on the driving voxels to the entire brain image for estimating the dense transformation for each subject. By iteratively repeating correspondence detection and dense transformation estimation, all the subjects will be aligned onto a common space simultaneously. Our groupwise registration algorithm has been extensively evaluated by 18 elderly brains, 16 NIREP, and 40 LONI data. In all experiments, our algorithm achieves more robust and accurate registration results, compared to a groupwise registration method and a pairwise registration method, respectively.

AB - We present a novel feature-based groupwise registration method to simultaneously warp the subjects towards the common space. Due to the complexity of the groupwise registration, we resort to decoupling it into two easy-to-solve tasks, i.e., alternatively establishing the robust correspondences across different subjects and interpolating the dense deformation fields based on the detected sparse correspondences. Specifically, several novel strategies are proposed in the correspondence detection step. First, attribute vector, instead of intensity only, is used as a morphological signature to guide the anatomical correspondence detection among all subjects. Second, we detect correspondence only on the driving voxels with distinctive attribute vectors for avoiding the ambiguity in detecting correspondences for non-distinctive voxels. Third, soft correspondence assignment (allowing for adaptive detection of multiple correspondences in each subject) is also presented to help establish reliable correspondences across all subjects, which is particularly necessary in the beginning of groupwise registration. Based on the sparse correspondences detected on the driving voxels of each subject, thin-plate splines (TPS) are then used to propagate the correspondences on the driving voxels to the entire brain image for estimating the dense transformation for each subject. By iteratively repeating correspondence detection and dense transformation estimation, all the subjects will be aligned onto a common space simultaneously. Our groupwise registration algorithm has been extensively evaluated by 18 elderly brains, 16 NIREP, and 40 LONI data. In all experiments, our algorithm achieves more robust and accurate registration results, compared to a groupwise registration method and a pairwise registration method, respectively.

UR - http://www.scopus.com/inward/record.url?scp=79956206521&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79956206521&partnerID=8YFLogxK

M3 - Chapter

C2 - 20879375

AN - SCOPUS:79956206521

VL - 13

SP - 684

EP - 691

BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

ER -