QCResUNet: Joint Subject-Level and Voxel-Level Prediction of Segmentation Quality
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Several methods have been proposed to address error regions detection, but they do not provide both segmentation error regions detection and multiple quality metrics estimations in 2D and 3D in a single framework.
“Several methods have been recently proposed to address the problem of error regions detection, e.g., Audelan and Delingette (2021); Zaman et al. (2023); Qiu et al. (2023). However, they do not provide both segmentation error regions detection and multiple quality metrics estimations in 2D and 3D in a single framework.”
From SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images (2024) · cites this paper as Qiu et al. (2023)
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · 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
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Specktor-Fadida, B., Ben-Sira, L., Ben-Bashat, D., & Joskowicz, L. (2024) · arXiv · Cited by 9 · see citing works
ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality
Cosarinsky, M., Billot, R., Mansilla, L., Jimenez, G., Gaggión, N., Fu, G., Tirer, T., & Ferrante, E. (2025) · arXiv
“QCResUNet [18] jointly predicts subject-level quality scores and voxel-level error maps using a multi-task deep network.”
- Authors
- Qiu, P., Chakrabarty, S., Nguyen, P., Ghosh, S. S., & Sotiras, A.
- Affiliations
- Washington University in St. Louis (US)
- University of Cincinnati (US)
- Corresponding author
- Qiu, Peijie
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 173-182
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4387211381
- Cited by
- 3
- References
- 4
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1
- FWCI
- 0.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- 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
- Keywords
- brain tumor segmentation, out-of-distribution samples, MRI segmentation, ResUNet
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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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