An artificial agent for robust image registration
Open accessAbstract
3-D image registration, which involves aligning two or more images, is a critical step in a variety of medical applications from diagnosis to therapy. Image registration is commonly performed by optimizing an image matching metric as a cost function. However this task is challenging due to the non-convex nature of the matching metric over the plausible registration parameter space and insufficient approches for a robust optimization. As a result, current approaches are often customized to a specific problem and sensitive to image quality and artifacts. In this paper, we propose a completely different approach to image registration, inspired by how experts perform the task. We first cast the image registration problem as a "strategic learning" process, where the goal is to find the best sequence of motion actions (e.g. up, down, etc) that yields image alignment. Within this approach, an artificial agent is learned, modeled using deep convolutional neural networks, with 3D raw image data as the input, and the next optimal action as the output. To copy with the dimensionality of the problem, we propose a greedy supervised approach for an end-to-end training, coupled with attention-driven hierarchical strategy. The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy). We demonstrate on two 3-D/3-D medical image registration examples with drastically different nature of challenges, that the artificial agent outperforms several state-of-the-art registration methods by a large margin in terms of both accuracy and robustness.
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Liao et al. firstly proposed to train an artificial agent for rigid registration of 3D CT images and cone-beam CT (CBCT) images for cardiac and abdominal images.
“Liao et al. (70) firstly proposed to train an artificial agent for the rigid registration of 3D CT images and cone-beam CT (CBCT) images for cardiac and abdominal images.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [70]
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- Authors
- Liao, R., Miao, S., De Tournemire, P., Grbic, S., Kamen, A., Mansi, T., & Comaniciu, D.
- Affiliations
- Siemens Healthcare (United States) (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2017-02-12
- Volume
- 31
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2963097316
- Cited by
- 199
- References
- 37
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 24 · 2023: 28 · 2022: 20 · 2021: 36 · 2020: 26 · 2019: 29 · 2018: 19 · 2017: 5
- FWCI
- 6.32
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- medical image registration, strategic learning, deep convolutional neural networks, non-convex optimization, robust optimization, end-to-end learning
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