Interactive segmentation of image volumes with Live Surface
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Machine learning methods for interactive segmentation can achieve higher segmentation accuracy with fewer user interactions, but are limited by hand-crafted features that depend on the user's experience.
“Machine learning methods [6], [37], [38] have been proposed to learn from user interactions. They can achieve higher segmentation accuracy with fewer user interactions. However, they are limited by hand-crafted features that depend on the user's experience.”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [6]
SlicSeg accepts user-provided scribbles in a single start slice to train an Online Random Forest for 3D segmentation, but lacks flexibility to allow further user-editing.
“SlicSeg [6] accepts user-provided scribbles in a single start slice to train an Online Random Forest for 3D segmentation, but lacks in flexibility to allow further user-editing.”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [6]
Interactive 2D slice-based segmentation is expected to achieve more robust results.
“Interactive 2D slice-based segmentation is expected to achieve more robust results [6], [55].”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [6]
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DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840695 · Cited by 502 · see citing works
- Authors
- Armstrong, C. J., Price, B. L., & Barrett, W. A.
- Affiliations
- Brigham Young University (US)
- Corresponding author
- Armstrong, Christopher J.
- Venue
- Computers & Graphics
- Publisher
- Elsevier BV
- Published
- 2007-04
- Volume
- 31
- Issue
- 2
- Pages
- 212-229
- ISSN
- 0097-8493 (ISSN-L)
- OpenAlex
- W2035509863
- Cited by
- 53
- References
- 31
- Citations by year
- 2021: 1 · 2019: 2 · 2018: 3 · 2017: 1 · 2016: 5 · 2015: 6 · 2014: 2 · 2013: 8 · 2012: 2
- FWCI
- 7.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers & Graphics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- interactive segmentation, 3D image volume, live wire, seeded region growing, 3D surface rendering, interactive rendering
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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.
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