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Cited by in F6Publishing
For: Liu M, Vanguri R, Mutasa S, Ha R, Liu YC, Button T, Jambawalikar S. Channel width optimized neural networks for liver and vessel segmentation in liver iron quantification. Comput Biol Med 2020;122:103798. [PMID: 32658724 DOI: 10.1016/j.compbiomed.2020.103798] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
Number Citing Articles
1 Zhang J, Wu F, Chang W, Kong D. Techniques and Algorithms for Hepatic Vessel Skeletonization in Medical Images: A Survey. Entropy 2022;24:465. [DOI: 10.3390/e24040465] [Reference Citation Analysis]
2 Gross M, Spektor M, Jaffe A, Kucukkaya AS, Iseke S, Haider SP, Strazzabosco M, Chapiro J, Onofrey JA. Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging. PLoS One 2021;16:e0260630. [PMID: 34852007 DOI: 10.1371/journal.pone.0260630] [Reference Citation Analysis]
3 Hill CE, Biasiolli L, Robson MD, Grau V, Pavlides M. Emerging artificial intelligence applications in liver magnetic resonance imaging. World J Gastroenterol 2021; 27(40): 6825-6843 [PMID: 34790009 DOI: 10.3748/wjg.v27.i40.6825] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
4 Wantanajittikul K, Saiviroonporn P, Saekho S, Krittayaphong R, Viprakasit V. An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data. BMC Med Imaging 2021;21:138. [PMID: 34583631 DOI: 10.1186/s12880-021-00669-2] [Reference Citation Analysis]
5 Xin M, Wen J, Wang Y, Yu W, Fang B, Hu J, Xu Y, Linghu C. Blood Vessel Segmentation Based on the 3D Residual U-Net. Int J Patt Recogn Artif Intell 2021;35:2157007. [DOI: 10.1142/s021800142157007x] [Reference Citation Analysis]