Deformable Image Registration Using Cue-aware Deep Regression Network

By Xiaohuan Cao, Jianhua Yang, Jun Zhang, Qian Wang, Pew-Thian Yap, Dinggang Shen. 2018

118 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mingxia Liu
2. Chunfeng Lian
3. Jun Zhang
4. Dinggang Shen
5. Arvid Lundervold

In Biomedical Engineering

1. Arvid Lundervold
2. Dinggang Shen
3. Chunfeng Lian
4. Jun Zhang
5. Mingxia Liu

In Computer Vision and Pattern Recognition

1. Arvid Lundervold
2. Jun Zhang
3. Mingxia Liu
4. Chunfeng Lian
5. Dinggang Shen
6. Heung-Il Suk
7. Yanqing Zhang
8. Jyoti Islam

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, background): “22 For example elastic registration between 3D MRI and transrectal ultrasound for guiding targeted prostate biopsy [95]; deformable registration for brain MRI where a “cue-aware deep regression network” learns from a given set of training images the displacement vector associated with a pair of refe”
• Deep learning in medical image registration: a review (2019, unknown): “[175] Brain 3D-3D Yes MR Deformable [10] Brain 3D-3D Yes MR Deformable”
• Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis using Structural MRI (2018, background): “Inrecent years, deep convolutional neural networks (CNNs) are showing increasingly successful applications in various medical image computing tasks [24], [25], [26], [27], [28], [29], [30].”
• Deep learning in medical image registration: a survey (2019, unknown): “[175] Brain 3D-3D Yes MR Deformable [10] Brain 3D-3D Yes MR Deformable”