The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation
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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
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
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
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
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 · cited 1×
“DeepMedic (Kamnitsas et al., 2017) and NiftyNet (Gibson et al.”
The Liver Tumor Segmentation Benchmark (LiTS)
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102680 · Cited by 1,412
Self-citationThe Liver Tumor Segmentation Benchmark (LiTS)
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2019) · arXiv · doi:10.48550/arxiv.1901.04056 · Cited by 1,412
Self-citationInterpretable Machine Learning in Healthcare
Ahmad, M. A., Eckert, C., & Teredesai, A. (2018) · Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics · doi:10.1145/3233547.3233667 · Cited by 583
NiftyNet: a deep-learning platform for medical imaging
Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T., Hu, Y., et al. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.025 · Cited by 552
Multi-modal learning from unpaired images: application to multi-organ segmentation in CT and MRI
Valindria, V. V., Pawlowski, N., Rajchl, M., Lavdas, I., Aboagye, E. O., Rockall, A. G., Rueckert, D., & Glocker, B. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00066 · Cited by 146
Cited by
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01008-z · Cited by 10,477
Self-citationThe Liver Tumor Segmentation Benchmark (LiTS)
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102680 · Cited by 1,412
Self-citationThe Liver Tumor Segmentation Benchmark (LiTS)
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2019) · arXiv · doi:10.48550/arxiv.1901.04056 · Cited by 1,412
Self-citationMedical image segmentation using deep learning: A survey
Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022) · IET Image Processing · doi:10.1049/ipr2.12419 · Cited by 892
Segment anything model for medical images?
Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103061 · Cited by 536
“Metrics Objects A1 [30] A4 [30] A5 [30] B3 [70] B4 [70] C8 [65] A4 [65]”
Influential
Metadata
- Authors
- Kavur, A. E., Gezer, N. S., Barış, M., Aslan, S., Conze, P.-H., Groza, V., Pham, D. D., Chatterjee, S., Ernst, P., Özkan, S., Baydar, B., Lachinov, D., Han, S., Pauli, J., Isensee, F., Perkonigg, M., Sathish, R., Rajan, R., Sheet, D., Dovletov, G., Speck, O., Nürnberger, A., Maier-Hein, K. H., Bozdağı Akar, G., Ünal, G., Dicle, O., & Selver, M. A.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 69
- Pages
- 101950
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W3002569343
- Cited by
- 815
- References
- 10
- FWCI
- 32.32
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation. https://app.cochat.ai/papers/10.1016/j.media.2020.101950
- The paper
- Kavur, A. E., Gezer, N. S., Barış, M., Aslan, S., Conze, P.-H., Groza, V., Pham, D. D., Chatterjee, S., Ernst, P., Özkan, S., Baydar, B., Lachinov, D., Han, S., Pauli, J., Isensee, F., Perkonigg, M., Sathish, R., Rajan, R., Sheet, D., . . . Selver, M. A. (2021). CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis. https://doi.org/10.1016/j.media.2020.101950
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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.
