The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
Open accessAbstract
Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To further improve the performance of vessel segmentation, we propose IterNet, a new model based on UNet [1], with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image. IterNet consists of multiple iterations of a mini-UNet, which can be 4× deeper than the common UNet. IterNet also adopts the weight-sharing and skip-connection features to facilitate training; therefore, even with such a large architecture, IterNet can still learn from merely 10~20 labeled images, without pre-training or any prior knowledge. IterNet achieves AUCs of 0.9816, 0.9851, and 0.9881 on three mainstream datasets, namely DRIVE, CHASE-DB1, and STARE, respectively, which currently are the best scores in the literature. The source code is available1.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Deformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513
Ridge-based vessel segmentation in color images of the retina
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.825627 · Cited by 4,202
Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response
Hoover, A. D., Kouznetsova, V., & Goldbaum, M. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.845178 · Cited by 2,582
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.-W., & Heng, P.-A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2845918 · Cited by 2,473
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., & Bengio, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.156 · Cited by 1,736
Weighted Res-UNet for High-Quality Retina Vessel Segmentation
Xiao, X., Lian, S., Luo, Z., & Li, S. (2018) · 2018 9th International Conference on Information Technology in Medicine and Education (ITME) · doi:10.1109/itme.2018.00080 · Cited by 963
DUNet: A deformable network for retinal vessel segmentation
Jin, Q., Meng, Z., Pham, T. D., Chen, Q., Wei, L., & Su, R. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.04.025 · Cited by 867
Cited by
SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation
Wu, H., Wang, W., Zhong, J., Lei, B., Wen, Z., & Qin, J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102025 · Cited by 259
Metadata
- Authors
- Li, L., Verma, M., Nakashima, Y., Nagahara, H., & Kawasaki, R.
- Venue
- 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2020-03
- Pages
- 3645-3654
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W3013766724
- Cited by
- 357
- References
- 23
- FWCI
- 62.38
- Type
- conference paper
- Language
- en
- Open access
- green
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks. https://app.cochat.ai/papers/10.1109/wacv45572.2020.9093621
- The paper
- Li, L., Verma, M., Nakashima, Y., Nagahara, H., & Kawasaki, R. (2020). IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv45572.2020.9093621
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
