Comparison of automatic liver volumetry performance using different types of magnetic resonance images.

ABSTRACT: Measurements of liver volume from MR images can be valuable for both clinical and research applications. Automated methods using convolutional neural networks have been used successfully for this using a variety of different MR image types as input. In this work, we sought to determine which types of magnetic resonance images give the best performance when used to train convolutional neural networks for liver segmentation and volumetry. Abdominal MRI scans were performed at 3 Tesla on 42 adolescents with obesity. Scans included Dixon imaging (giving water, fat, and T2* images) and low-resolution T2-weighted scout images. Multiple convolutional neural network models using a 3D U-Net architecture were trained with different input images. Whole-liver manual segmentations were used for referen

SUBMITTER: Saunders SL 

PROVIDER: S-EPMC9812021 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature