Abstract
Significance: Analysis of modern large-scale, multicenter or diseased data requires deformable registration algorithms that can cope with data of diverse nature. Objective: We propose a novel deformable registration method, which is based on a cue-aware deep regression network, to deal with multiple databases with minimal parameter tuning. Methods: Our method learns and predicts the deformation field between a reference image and a subject image. Specifically, given a set of training images, our method learns the displacement vector associated with a pair of reference-subject patches. To achieve this, we first introduce a key-point truncated-balanced sampling strategy to facilitate accurate learning from the image database of limited size. Then, we design a cue-aware deep regression network, where we propose to employ the contextual cue, i.e., the scale-adaptive local similarity, to more apparently guide the learning process. The deep regression network is aware of the contextual cue for accurate prediction of local deformation. Results and Conclusion: Our experiments show that the proposed method can tackle various registration tasks on different databases, giving consistent good performance without the need of manual parameter tuning, which could be applicable to various clinical applications.
| Original language | English |
|---|---|
| Article number | 8331111 |
| Pages (from-to) | 1900-1911 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 65 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 2018 Sep 1 |
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Keywords
- Deep learning
- Deformable registration
- Key-points sampling
- Nonlinear regression
ASJC Scopus subject areas
- Biomedical Engineering
Cite this
Deformable image registration using a cue-aware deep regression network. / Cao, Xiaohuan; Yang, Jianhua; Zhang, Jun; Wang, Qian; Yap, Pew Thian; Shen, Dinggang.
In: IEEE Transactions on Biomedical Engineering, Vol. 65, No. 9, 8331111, 01.09.2018, p. 1900-1911.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Deformable image registration using a cue-aware deep regression network
AU - Cao, Xiaohuan
AU - Yang, Jianhua
AU - Zhang, Jun
AU - Wang, Qian
AU - Yap, Pew Thian
AU - Shen, Dinggang
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Significance: Analysis of modern large-scale, multicenter or diseased data requires deformable registration algorithms that can cope with data of diverse nature. Objective: We propose a novel deformable registration method, which is based on a cue-aware deep regression network, to deal with multiple databases with minimal parameter tuning. Methods: Our method learns and predicts the deformation field between a reference image and a subject image. Specifically, given a set of training images, our method learns the displacement vector associated with a pair of reference-subject patches. To achieve this, we first introduce a key-point truncated-balanced sampling strategy to facilitate accurate learning from the image database of limited size. Then, we design a cue-aware deep regression network, where we propose to employ the contextual cue, i.e., the scale-adaptive local similarity, to more apparently guide the learning process. The deep regression network is aware of the contextual cue for accurate prediction of local deformation. Results and Conclusion: Our experiments show that the proposed method can tackle various registration tasks on different databases, giving consistent good performance without the need of manual parameter tuning, which could be applicable to various clinical applications.
AB - Significance: Analysis of modern large-scale, multicenter or diseased data requires deformable registration algorithms that can cope with data of diverse nature. Objective: We propose a novel deformable registration method, which is based on a cue-aware deep regression network, to deal with multiple databases with minimal parameter tuning. Methods: Our method learns and predicts the deformation field between a reference image and a subject image. Specifically, given a set of training images, our method learns the displacement vector associated with a pair of reference-subject patches. To achieve this, we first introduce a key-point truncated-balanced sampling strategy to facilitate accurate learning from the image database of limited size. Then, we design a cue-aware deep regression network, where we propose to employ the contextual cue, i.e., the scale-adaptive local similarity, to more apparently guide the learning process. The deep regression network is aware of the contextual cue for accurate prediction of local deformation. Results and Conclusion: Our experiments show that the proposed method can tackle various registration tasks on different databases, giving consistent good performance without the need of manual parameter tuning, which could be applicable to various clinical applications.
KW - Deep learning
KW - Deformable registration
KW - Key-points sampling
KW - Nonlinear regression
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UR - http://www.scopus.com/inward/citedby.url?scp=85052623089&partnerID=8YFLogxK
U2 - 10.1109/TBME.2018.2822826
DO - 10.1109/TBME.2018.2822826
M3 - Article
VL - 65
SP - 1900
EP - 1911
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 9
M1 - 8331111
ER -