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A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet++ by Jing Wang; Yanyang Peng; Shi Jing; Lujun Han; Tian Li; Junpeng Luo is a Medicine article available to read on EtoBox.

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## 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 + + network, and the segmented liver mask was re-input as the input end into the UNet + + network to segment liver tumors. The false positivity rate was reduced using a threshold value in the liver tumor segmentation. To evaluate the segmentation results, we calculated the Dice similarity coefficient (DSC), average false positivity rate (AFPR), and delineation time. ## Results The average DSC of the liver in the va

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Author

Jing Wang; Yanyang Peng; Shi Jing; Lujun Han; Tian Li; Junpeng Luo

Publisher

Springer Science and Business Media LLC

Published

2023

Field

Medicine (Health Sciences)