MS-FANet: Multi-scale feature attention network for liver tumor segmentation

Chen, Ying; Zheng, Cheng; Zhang, Wei; Lin, Hongping; Chen, Wang; Zhang, Guimei; Xu, Gelin; Wu, Fang

doi:10.1016/j.compbiomed.2023.107208

Cited by 8 publications

(1 citation statement)

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“…The type of deep-learning model determines the content and accuracy of the segmentation results. In the field of liver and liver tumor segmentation, several recent studies [13][14][15][16][17] report computed tomography (CT)-based liver segmentation using deep learning methods, and the segmentation performance appears to be good [18]. Hepatocellular carcinoma is currently diagnosed and evaluated using magnetic resonance imaging (MRI), which is more sensitive and specific than CT for lesions with diameter < 3 cm and equivalent to CT for larger lesions [19].…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Peng,

Jing

et al. 2023

BMC Cancer

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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 validation and internal testing sets was 0.91 and 0.92, respectively. In the validation set, manual and automatic delineation took 182.9 and 2.2 s, respectively. On an average, manual and automatic delineation took 169.8 and 1.7 s, respectively. The average DSC of liver tumors was 0.612 and 0.687 in the validation and internal testing sets, respectively. The average time for manual and automatic delineation and AFPR in the internal testing set were 47.4 s, 2.9 s, and 1.4, respectively, and those in the external test set were 29.5 s, 4.2 s, and 1.6, respectively. Conclusion UNet + + can automatically segment normal hepatic tissue and liver tumors based on MR images. It provides a methodological basis for the automated segmentation of liver tumors, improves the delineation efficiency, and meets the requirement of extraction set analysis of further radiomics and deep learning.

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