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全自动术前肝脏体积测量,包含中央肝静脉的解剖位置。

Fully automated preoperative liver volumetry incorporating the anatomical location of the central hepatic vein.

机构信息

Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.

Institute of Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.

出版信息

Sci Rep. 2022 Oct 1;12(1):16479. doi: 10.1038/s41598-022-20778-4.

DOI:10.1038/s41598-022-20778-4
PMID:36183002
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9526715/
Abstract

The precise preoperative calculation of functional liver volumes is essential prior major liver resections, as well as for the evaluation of a suitable donor for living donor liver transplantation. The aim of this study was to develop a fully automated, reproducible, and quantitative 3D volumetry of the liver from standard CT examinations of the abdomen as part of routine clinical imaging. Therefore, an in-house dataset of 100 venous phase CT examinations for training and 30 venous phase ex-house CT examinations with a slice thickness of 5 mm for testing and validating were fully annotated with right and left liver lobe. Multi-Resolution U-Net 3D neural networks were employed for segmenting these liver regions. The Sørensen-Dice coefficient was greater than 0.9726 ± 0.0058, 0.9639 ± 0.0088, and 0.9223 ± 0.0187 and a mean volume difference of 32.12 ± 19.40 ml, 22.68 ± 21.67 ml, and 9.44 ± 27.08 ml compared to the standard of reference (SoR) liver, right lobe, and left lobe annotation was achieved. Our results show that fully automated 3D volumetry of the liver on routine CT imaging can provide reproducible, quantitative, fast and accurate results without needing any examiner in the preoperative work-up for hepatobiliary surgery and especially for living donor liver transplantation.

摘要

在进行大型肝切除术之前以及评估活体供肝移植的合适供体时,精确地术前计算功能性肝体积至关重要。本研究的目的是开发一种完全自动化、可重复、定量的 3D 肝脏体积测量方法,该方法基于腹部标准 CT 检查,作为常规临床成像的一部分。因此,我们建立了一个内部数据集,其中包括 100 例静脉期 CT 检查用于训练,以及 30 例静脉期外院 CT 检查(层厚 5mm)用于测试和验证,这些检查均对右肝叶和左肝叶进行了完全标注。使用多分辨率 U-Net 3D 神经网络对这些肝脏区域进行分割。Sørensen-Dice 系数大于 0.9726±0.0058、0.9639±0.0088 和 0.9223±0.0187,与标准参考(SoR)肝脏、右叶和左叶标注相比,平均体积差异为 32.12±19.40ml、22.68±21.67ml 和 9.44±27.08ml。我们的结果表明,在常规 CT 成像上进行全自动肝脏 3D 体积测量可以提供可重复、定量、快速和准确的结果,而无需在术前评估中进行任何检查,这对于肝胆手术,特别是活体供肝移植非常重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/73a19b0c429f/41598_2022_20778_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/81a65a8a88c6/41598_2022_20778_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/195b0d6eaf07/41598_2022_20778_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/c83b61bd37d0/41598_2022_20778_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/c3060f1e167b/41598_2022_20778_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/734d27a429a7/41598_2022_20778_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/73a19b0c429f/41598_2022_20778_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/81a65a8a88c6/41598_2022_20778_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/195b0d6eaf07/41598_2022_20778_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/c83b61bd37d0/41598_2022_20778_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/c3060f1e167b/41598_2022_20778_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/734d27a429a7/41598_2022_20778_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b625/9526715/73a19b0c429f/41598_2022_20778_Fig6_HTML.jpg

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