Abstract

Objectives

To develop and validate a deep learning (DL) model for automated segmentation of hepatic and portal veins, and apply the model in blood-free future liver remnant (FLR) assessments via CT before major hepatectomy.

Methods

3-dimensional 3D U-Net models were developed for the automatic segmentation of hepatic veins and portal veins on contrast-enhanced CT images. A total of 170 patients treated from January 2018 to March 2019 were included. 3D U-Net models were trained and tested under various liver conditions. The Dice similarity coefficient (DSC) and volumetric similarity (VS) were used to evaluate the segmentation accuracy. The use of quantitative volumetry for evaluating resection was compared between blood-filled and blood-free settings and between manual and automated segmentation.

Results

The DSC values in the test dataset for hepatic veins and portal veins were 0.66 ± 0.08 (95% CI: (0.65, 0.68)) and 0.67 ± 0.07 (95% CI: (0.66, 0.69)), the VS values were 0.80 ± 0.10 (95% CI: (0.79, 0.84)) and 0.74 ± 0.08 (95% CI: (0.73, 0.76)), respectively No significant differences in FLR, FLR% assessments, or the percentage of major hepatectomy patients were noted between the blood-filled and blood-free settings (p = 0.67, 0.59 and 0.99 for manual methods, p = 0.66, 0.99 and 0.99 for automated methods, respectively) according to the use of manual and automated segmentation methods.

Conclusion

Fully automated segmentation of hepatic veins and portal veins and FLR assessment via blood-free CT before major hepatectomy are accurate and applicable in clinical cases involving the use of DL.

Critical relevance statement

Our fully automatic models could segment hepatic veins, portal veins, and future liver remnant in blood-free setting on CT images before major hepatectomy with reliable outcomes.

Key Points

Fully automatic segmentation of hepatic veins and portal veins was feasible in clinical practice.

Fully automatic volumetry of future liver remnant (FLR)% in a blood-free setting was robust.

No significant differences in FLR% assessments were noted between the blood-filled and blood-free settings.

Details

Title
Fully automated assessment of the future liver remnant in a blood-free setting via CT before major hepatectomy via deep learning
Author
Xie, Tingting 1 ; Zhou, Jingyu 1 ; Zhang, Xiaodong 2 ; Zhang, Yaofeng 3 ; Wang, Xiaoying 2 ; Li, Yongbin 4   VIAFID ORCID Logo  ; Cheng, Guanxun 1 

 Peking University Shenzhen Hospital, Medical Imaging Center, Shenzhen, China (GRID:grid.440601.7) (ISNI:0000 0004 1798 0578) 
 Peking University First Hospital, Department of Radiology, Beijing, China (GRID:grid.411472.5) (ISNI:0000 0004 1764 1621) 
 Beijing Smart Tree Medical Technology Co. Ltd, Beijing, China (GRID:grid.411472.5) 
 Peking University Shenzhen Hospital, Department of Ultrasound, Shenzhen, China (GRID:grid.440601.7) (ISNI:0000 0004 1798 0578) 
Pages
164
Publication year
2024
Publication date
Dec 2024
Publisher
Springer Nature B.V.
e-ISSN
18694101
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3072929045
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.