Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies.

MLA

Patel, Nihil, et al. “Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles of Datasets with Different Contrast Protocols and Liver Disease Etiologies.” Scientific Reports, vol. 14, no. 1, Sept. 2024, pp. 1–14. EBSCOhost, https://doi.org/10.1038/s41598-024-71674-y.

APA

Patel, N., Celaya, A., Eltaher, M., Glenn, R., Savannah, K. B., Brock, K. K., Sanchez, J. I., Calderone, T. L., Cleere, D., Elsaiey, A., Cagley, M., Gupta, N., Victor, D., Beretta, L., Koay, E. J., Netherton, T. J., & Fuentes, D. T. (2024). Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies. Scientific Reports, 14(1), 1–14. https://doi.org/10.1038/s41598-024-71674-y

Chicago

Patel, Nihil, Adrian Celaya, Mohamed Eltaher, Rachel Glenn, Kari Brewer Savannah, Kristy K. Brock, Jessica I. Sanchez, et al. 2024. “Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles of Datasets with Different Contrast Protocols and Liver Disease Etiologies.” Scientific Reports 14 (1): 1–14. doi:10.1038/s41598-024-71674-y.