Cited by 113Medical Image Segmentation TechniquesMedical Imaging and AnalysisAdvanced Neural Network Applications

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Dilated FCN for Multi-Agent 2D/3D Medical Image Registration

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Miao, Shun, Piat, Sebastien, Fischer, Peter, Tuysuzoglu, Ahmet, Mewes, Philip, Mansi, Tommaso, Liao, Rui·2018·Proceedings of the AAAI Conference on Artificial Intelligence

DOI 10.1609/aaai.v32i1.11576

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Abstract

2D/3D image registration to align a 3D volume and 2D X-ray images is a challenging problem due to its ill-posed nature and various artifacts presented in 2D X-ray images. In this paper, we propose a multi-agent system with an auto attention mechanism for robust and efficient 2D/3D image registration. Specifically, an individual agent is trained with dilated Fully Convolutional Network (FCN) to perform registration in a Markov Decision Process (MDP) by observing a local region, and the final action is then taken based on the proposals from multiple agents and weighted by their corresponding confidence levels. The contributions of this paper are threefold. First, we formulate 2D/3D registration as a MDP with observations, actions, and rewards properly defined with respect to X-ray imaging systems. Second, to handle various artifacts in 2D X-ray images, multiple local agents are employed efficiently via FCN-based structures, and an auto attention mechanism is proposed to favor the proposals from regions with more reliable visual cues. Third, a dilated FCN-based training mechanism is proposed to significantly reduce the Degree of Freedom in the simulation of registration environment, and drastically improve training efficiency by an order of magnitude compared to standard CNN-based training method. We demonstrate that the proposed method achieves high robustness on both spine cone beam Computed Tomography data with a low signal-to-noise ratio and data from minimally invasive spine surgery where severe image artifacts and occlusions are presented due to metal screws and guide wires, outperforming other state-of-the-art methods (single agent-based and optimization-based) by a large margin.

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Authors: Miao, S., Piat, S., Fischer, P., Tuysuzoglu, A., Mewes, P., Mansi, T., & Liao, R.
Affiliations: Siemens Healthcare (United States) (US)
ORCID: Miao, Shun
Piat, Sebastien
Fischer, Peter
Mansi, Tommaso
Liao, Rui
Venue: Proceedings of the AAAI Conference on Artificial Intelligence
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Published: 2018-04-26
Volume: 32
Issue: 1
ISSN: 2159-5399 (ISSN-L)
2374-3468
DOI: 10.1609/aaai.v32i1.11576
OpenAlex: W2962759210
Cited by: 113
References: 30
Citations by year: 2026: 3 · 2025: 5 · 2024: 12 · 2023: 18 · 2022: 17 · 2021: 29 · 2020: 21 · 2019: 6 · 2018: 2
FWCI: 4.37
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
Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: 2D-3D registration, multi-agent reinforcement learning, Markov decision process, X-ray images, cone-beam computed tomography, spinal surgery, metal artifact reduction, occlusion handling, minimally invasive spine surgery, low signal-to-noise ratio

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Dilated FCN for Multi-Agent 2D/3D Medical Image Registration

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