Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics.

ABSTRACT:

Objectives

To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on contrast-enhanced portal venous phase magnetic resonance imaging (MRI).

Materials and methods

This retrospective study included hepatocellular carcinoma patients from an institutional database with portal venous MRI. After manual segmentation, the data was randomly split into independent training, validation, and internal testing sets. From a collaborating institution, de-identified scans were used for external testing. The public LiverHccSeg dataset was used for further external validation. A 3D DCNN was trained to automatically segment the liver. Segmentation accuracy was quantified by the Dice similarity coefficient

SUBMITTER: Gross M 

PROVIDER: S-EPMC11245591 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature