Image segmentation evaluation: A survey of unsupervised methods
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The application of unsupervised methods in medical settings is unclear.
“While unsupervised methods can be applied to scenarios where the main purpose of segmentation is to yield visually consistent results that are meaningful to a human observer, the application in medical settings is unclear.”
From Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth (2017) · cites this paper as [25]
Cited by
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Zakeri, H., Nejad, F. M., & Fahimifar, A. (2017) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-016-9194-z · Cited by 304 · 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
Specktor-Fadida, B., Ben-Sira, L., Ben-Bashat, D., & Joskowicz, L. (2024) · arXiv · Cited by 9 · see citing works
Comparison of algorithms for ultrasound image segmentation without ground truth
Sikka, K., & Deserno, T. M. (2010) · SPIE Proceedings · doi:10.1117/12.844504 · Cited by 4 · see citing works
- Authors
- Zhang, H., Fritts, J. E., & Goldman, S. A.
- Affiliations
- Washington University in St. Louis (US)
- Saint Louis University (US)
- Corresponding author
- Zhang, Hui
- ORCID
- Zhang, Hui
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2008-05
- Volume
- 110
- Issue
- 2
- Pages
- 260-280
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2008043556
- Cited by
- 963
- References
- 0
- Citations by year
- 2026: 10 · 2025: 19 · 2024: 22 · 2023: 38 · 2022: 31 · 2021: 40 · 2020: 56 · 2019: 66 · 2018: 69 · 2017: 68 · 2016: 69 · 2015: 81 · 2014: 95 · 2013: 81 · 2012: 79
- FWCI
- 32.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Vision and Image Understanding (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Keywords
- image segmentation, unsupervised evaluation, segmentation evaluation, objective evaluation metrics, segmentation algorithm comparison, subjective evaluation
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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