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Precise whole liver automatic segmentation and quantification of PDFF and R2* on MR images
Liver diseases like NAFLD and NASH present diagnostic and monitoring challenges, especially in evaluating crucial liver deposits of fat and iron. Current liver segmentation methods in MRI analysis face issues with computational costs, execution times, and adaptability to varied liver shapes and sizes. This paper introduces a CNN-based methodology for automatic liver segmentation and quantification in MRIs, providing a promising solution to these challenges. The model, employing an encoder-decoder structure, deep supervision, and normalization techniques, is trained using data augmentation and a 5-fold cross-validation strategy. Demonstrating high accuracy and low relative errors in quantifying vital liver biomarkers and with successful external validation across different imaging settings, this methodology offers a reliable, efficient, and widely applicable solution for liver and fat quantification in MRIs, enhancing clinical evaluations and facilitating further advancements in liver disease research and treatment.
https://www.mdpi.com/2072-6694/15/16/4163
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 Oct;31(10):7876-7887. doi: 10.1007/s00330-021-07838-5. Epub 2021 Mar 25. PMID: 33768292.