Automatic Cone Beam Projection-Based Liver Tumor Localization by Deep Learning and Biomechanical Modeling
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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 Zhang et al., 2020
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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
“…that were used included the standard registration pose error (commonly separated into translational and rotational DoFs), keypoint distances [52, 48], and the mean target registration error (mTRE) in 3D, and varied other metrics in 2D, such as reprojection distances [41], segmentation or…”
BackgroundMethodInfluential
- Authors
- Zhang, Y., Huang, X., & Wang, J.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Venue
- International Journal of Radiation Oncology*Biology*Physics
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 108
- Issue
- 3
- Pages
- S171
- ISSN
- 0360-3016 (ISSN-L)
- OpenAlex
- W3094379937
- Cited by
- 5
- References
- 0
- Citations by year
- 2025: 1 · 2024: 2 · 2022: 1 · 2021: 1
- FWCI
- 0.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Radiation Oncology*Biology*Physics (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- cone-beam computed tomography, image-guided radiotherapy, deformable image registration, contour propagation, biomechanical modeling, patient-specific modeling, on-board imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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