Local Regression Learning via Forest Classification for 2D/3D Deformable Registration
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The error metrics used in the reviewed studies included standard registration pose error, keypoint distances, mean target registration error (mTRE) in 3D, and varied 2D metrics such as reprojection distances, DICE score, and contour differences.
“The error metrics that were used included the standard registration pose error (commonly separated into translational and rotational DoFs), keypoint distances (Chou and Pizer, 2014; Zhang et al., 2020), and the mean target registration error (mTRE) in 3D, and varied other metrics in 2D, such as reprojection distances (Bier et al., 2019), segmentation or overlap DICE score (Zhang et al., 2020), or”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Chou and Pizer, 2014
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Normalized cuts and image segmentation
Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,842 · see citing works
Decision Forests for Computer Vision and Medical Image Analysis
Criminisi, A., & Shotton, J. (2013) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-4929-3 · Cited by 610 · see citing works
2D/3D image registration using regression learning
Chou, C.-R., Frederick, C. B., Mageras, G. S., Chang, S., & Pizer, S. M. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.02.009 · Cited by 62 · see citing works
Self-citationReal-Time 2D/3D Deformable Registration Using Metric Learning
Chou, C.-R., & Pizer, S. M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36620-8_1 · Cited by 11 · see citing works
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Unberath, M., Gao, C., Hu, Y., Judish, M., Taylor, R. H., Armand, M., & Grupp, R. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.716007 · Cited by 102 · see citing works
“We have identified 22 studies that describe methods for direct parameter regression [66, 67, 68, 69, 70, 49, 51, 52, 53, 71, 72, 73, 74, 54, 55, 56, 57, 75, 65, 76, 77, 78].”
BackgroundMethodInfluentialLocal Metric Learning in 2D/3D Deformable Registration With Application in the Abdomen
Zhao, Q., Chou, C.-R., Mageras, G. S., & Pizer, S. M. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2319193 · Cited by 18 · see citing works
Self-citationMedical Computer Vision. Large Data in Medical Imaging
Menze, B. H., Tu, Z., Müller, H., Kelm, M., Montillo, A., & Langs, G. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-05530-5 · Cited by 13 · see citing works
Jiang, Y., Pei, Y., Xu, T., Yuan, X., & Zha, H. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3456251 · Cited by 2 · see citing works
Overview of the 2013 Workshop on Medical Computer Vision (MCV 2013)
Müller, H., Menze, B. H., Langs, G., Montillo, A., Kelm, B. M., Zhuowen Tu, A., & del Toro, Ó. A. J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-05530-5_1 · Cited by 2 · see citing works
- Authors
- Chou, C.-R., & Pizer, S.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Chou, Chen-Rui
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2014
- Pages
- 24-33
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W245150054
- Cited by
- 5
- References
- 5
- Citations by year
- 2024: 1 · 2021: 1 · 2014: 3
- FWCI
- 0.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- deformable image registration, 2D-3D registration, lung deformation, local regression, graph cuts, real-time registration
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