Towards fully automatic image segmentation evaluation
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Existing methods for evaluating segmentation without ground truth cannot be applied to a general task with many different class labels.
“However, these methods cannot be applied to a more general task, such as an image with many different class labels to be segmented.”
From Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth (2017) · cites this paper as [20]
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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
- Goldmann, L., Adamek, T., Vajda, P., Karaman, M., Mörzinger, R., Galmar, E., Sikora, T., O’Connor, N. E., Ha-Minh, T., Ebrahimi, T., Schallauer, P., & Huet, B.
- Affiliations
- Dublin City University (IE)
- Technische Universität Berlin (DE)
- Joanneum Research (AT)
- EURECOM (FR)
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008
- Pages
- 566-577
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2158519562
- Cited by
- 18
- References
- 0
- Citations by year
- 2024: 1 · 2020: 1 · 2018: 2 · 2017: 1 · 2016: 3 · 2015: 1 · 2014: 3 · 2012: 1
- FWCI
- 1.33
- Type
- conference paper
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- Lecture notes in computer science (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- 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 › Visual Attention and Saliency Detection
- Keywords
- image segmentation evaluation, background subtraction, foreground-background segmentation, video segmentation, segmentation performance comparison
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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