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Cited by in F6Publishing
For: Jimenez-Pastor A, Alberich-Bayarri A, Lopez-Gonzalez R, Marti-Aguado D, França M, Bachmann RSM, Mazzucco J, Marti-Bonmati L. Precise whole liver automatic segmentation and quantification of PDFF and R2* on MR images. Eur Radiol 2021. [PMID: 33768292 DOI: 10.1007/s00330-021-07838-5] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 deSouza NM, van der Lugt A, Deroose CM, Alberich-Bayarri A, Bidaut L, Fournier L, Costaridou L, Oprea-Lager DE, Kotter E, Smits M, Mayerhoefer ME, Boellaard R, Caroli A, de Geus-Oei LF, Kunz WG, Oei EH, Lecouvet F, Franca M, Loewe C, Lopci E, Caramella C, Persson A, Golay X, Dewey M, O'Connor JPB, deGraaf P, Gatidis S, Zahlmann G; European Society of Radiology., European Organisation for Research and Treatment of Cancer. Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC. Insights Imaging 2022;13:159. [PMID: 36194301 DOI: 10.1186/s13244-022-01287-4] [Reference Citation Analysis]
2 Robinson-Weiss C, Patel J, Bizzo BC, Glazer DI, Bridge CP, Andriole KP, Dabiri B, Chin JK, Dreyer K, Kalpathy-Cramer J, Mayo-Smith WW. Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT. Radiology 2022;:220101. [PMID: 36125375 DOI: 10.1148/radiol.220101] [Reference Citation Analysis]
3 Silva RDCD, Jenkyn TR, Carranza VA. Enhanced Pre-Processing for Deep Learning in MRI Whole Brain Segmentation using Orthogonal Moments. Brain Multiphysics 2022. [DOI: 10.1016/j.brain.2022.100049] [Reference Citation Analysis]
4 Vernikouskaya I, Müller HP, Felbel D, Roselli F, Ludolph AC, Kassubek J, Rasche V. Body fat compartment determination by encoder-decoder convolutional neural network: application to amyotrophic lateral sclerosis. Sci Rep 2022;12:5513. [PMID: 35365743 DOI: 10.1038/s41598-022-09518-w] [Reference Citation Analysis]
5 Liu D, Lin C, Liu B, Qi J, Wen H, Tu L, Wei Q, Kong Q, Xie Y, Gu J. Quantification of Fat Metaplasia in the Sacroiliac Joints of Patients With Axial Spondyloarthritis by Chemical Shift-Encoded MRI: A Diagnostic Trial. Front Immunol 2022;12:811672. [DOI: 10.3389/fimmu.2021.811672] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 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]
7 Martí-Aguado D, Jiménez-Pastor A, Alberich-Bayarri Á, Rodríguez-Ortega A, Alfaro-Cervello C, Mestre-Alagarda C, Bauza M, Gallén-Peris A, Valero-Pérez E, Ballester MP, Gimeno-Torres M, Pérez-Girbés A, Benlloch S, Pérez-Rojas J, Puglia V, Ferrández A, Aguilera V, Escudero-García D, Serra MA, Martí-Bonmatí L. Automated Whole-Liver MRI Segmentation to Assess Steatosis and Iron Quantification in Chronic Liver Disease. Radiology 2021;:211027. [PMID: 34783592 DOI: 10.1148/radiol.2021211027] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
8 Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. Vis Comput 2021;:1-16. [PMID: 34744231 DOI: 10.1007/s00371-021-02322-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]