Automating image segmentation verification and validation by learning test oracles
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Approaches [31], [33] can only be applied when a training database with good and bad segmentations is available.
“However, the approaches [31], [33] can only be applied when a training database with good and bad segmentations is available from which a mapping from features to segmentation accuracy can be learned.”
From Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth (2017) · cites this paper as [33]
Unlike the supervised methods in [33] and [31], RCA requires no training data that captures examples of good and bad segmentations.
“An appealing property of the proposed framework is that unlike the supervised methods in [33] and [31] no training data is required that captures examples of good and bad segmentations.”
From Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth (2017) · cites this paper as [33]
Cites
Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis
Crum, W. R., Camara, O., & Hill, D. L. G. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.880587 · Cited by 776 · see citing works
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The oracle problem in software testing: A survey
Barr, E. T., Harman, M., McMinn, P., Shahbaz, M., & Yoo, S. (2015) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2014.2372785 · Cited by 1,104 · see citing works
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
Valindria, V. V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E. O., Rockall, A. G., Rueckert, D., & Glocker, B. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2665165 · Cited by 152 · see citing works
- Authors
- Frounchi, K., Briand, L. C., Grady, L., Labiche, Y., & Subramanyan, R.
- Affiliations
- Carleton University (CA)
- University of Oslo (NO)
- Siemens (United States) (US)
- Corresponding author
- Frounchi, Kambiz
- Venue
- Information and Software Technology
- Publisher
- Elsevier BV
- Published
- 2011-12
- Volume
- 53
- Issue
- 12
- Pages
- 1337-1348
- ISSN
- 0950-5849 (ISSN-L)
- OpenAlex
- W1963712724
- Cited by
- 31
- References
- 1
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 6 · 2021: 1 · 2020: 2 · 2018: 1 · 2017: 4 · 2016: 2 · 2015: 3 · 2014: 4 · 2013: 3 · 2012: 1
- FWCI
- 3.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information and Software Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image segmentation, verification and validation, similarity measures, 3D segmentation, computed tomography, software testing, medical image analysis
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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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