A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet+.

ABSTRACT:

Objective

Radiomic and deep learning studies based on magnetic resonance imaging (MRI) of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and tumor exhibits limitations.

Methods

105 patients diagnosed with hepatocellular carcinoma were retrospectively studied between Jan 2015 and Dec 2020. The patients were divided into three sets: training (n = 83), validation (n = 11), and internal testing (n = 11). Additionally, 9 cases were included from the Cancer Imaging Archive as the external test set. Using the arterial phase and T2WI sequences, expert radiologists manually delineated all images. Using deep learning, liver tumors and liver segments were automatically segmented. A preliminary liver segmentation was performed using the UNet + + netwo

SUBMITTER: Wang J 

PROVIDER: S-EPMC10623778 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature