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1
Dr. Liver: A preoperative planning system of liver graft volumetry for living donor liver transplantation.李博士:活体肝移植供肝体积测量的术前规划系统。
Comput Methods Programs Biomed. 2018 May;158:11-19. doi: 10.1016/j.cmpb.2018.01.024. Epub 2018 Jan 31.
2
3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy -Means and Graph Cuts.基于改进的模糊均值和图割的 CT 图像肝脏肿瘤三维分割。
Biomed Res Int. 2017;2017:5207685. doi: 10.1155/2017/5207685. Epub 2017 Sep 26.
3
Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching.使用图割和边界推进从腹部CT容积中自动分割肝脏。
Comput Methods Programs Biomed. 2017 May;143:1-12. doi: 10.1016/j.cmpb.2017.02.015. Epub 2017 Feb 27.
4
Liver vessel segmentation based on extreme learning machine.基于极限学习机的肝血管分割。
Phys Med. 2016 May;32(5):709-16. doi: 10.1016/j.ejmp.2016.04.003. Epub 2016 May 4.
5
Liver surgery in cirrhosis and portal hypertension.肝硬化和门静脉高压症的肝脏手术
World J Gastroenterol. 2016 Mar 7;22(9):2725-35. doi: 10.3748/wjg.v22.i9.2725.
6
Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images.基于形状约束和可变形图割的 CT 图像肝脏自动分割。
IEEE Trans Image Process. 2015 Dec;24(12):5315-29. doi: 10.1109/TIP.2015.2481326. Epub 2015 Sep 23.
7
Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images.腹部CTA图像上肝脏血管分割的降噪和血管性滤波器的定量评估
Phys Med Biol. 2015 May 21;60(10):3905-26. doi: 10.1088/0031-9155/60/10/3905. Epub 2015 Apr 24.
8
3D vasculature segmentation using localized hybrid level-set method.使用局部混合水平集方法的三维血管分割
Biomed Eng Online. 2014 Dec 16;13:169. doi: 10.1186/1475-925X-13-169.
9
Accuracy of estimation of graft size for living-related liver transplantation: first results of a semi-automated interactive software for CT-volumetry.活体亲属肝移植中移植物大小估计的准确性:CT容积测量半自动交互式软件的初步结果
PLoS One. 2014 Oct 17;9(10):e110201. doi: 10.1371/journal.pone.0110201. eCollection 2014.
10
A multiatlas segmentation using graph cuts with applications to liver segmentation in CT scans.一种使用图割的多图谱分割方法及其在CT扫描肝脏分割中的应用。
Comput Math Methods Med. 2014;2014:182909. doi: 10.1155/2014/182909. Epub 2014 Sep 8.

肝脏肿瘤切除规划系统综述:步骤、技术与参数

Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters.

作者信息

Alirr Omar Ibrahim, Rahni Ashrani Aizzuddin Abd

机构信息

Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia.

出版信息

J Digit Imaging. 2020 Apr;33(2):304-323. doi: 10.1007/s10278-019-00262-8.

DOI:10.1007/s10278-019-00262-8
PMID:31428898
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7165210/
Abstract

Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.

摘要

肝脏手术治疗的术前规划是一种重要的规划工具,有助于降低手术切除的风险。基于计算机断层扫描(CT)图像,可以在实际肿瘤切除手术前规划切除方案。计算机辅助系统提供了肝脏器官及其内部结构、肿瘤和脉管系统空间关系的概述。它还能准确计算切除后剩余肝脏体积。本文旨在回顾计算机辅助系统的主要阶段,该系统有助于评估肝癌手术治疗期间的切除风险。计算机辅助系统通过使医生能够进行体积测量并可视化肝脏、肿瘤和周围脉管系统来协助手术规划。本文得出结论,为了准确规划肿瘤切除,应分割肝脏器官及其内部结构,以了解它们之间清晰的空间关系,从而实现更安全的切除。本文介绍了计算机辅助系统中每个阶段主要的分割技术,即肝脏器官、肿瘤和血管的分割技术。从所回顾的方法中发现,不依赖单一特定技术,而是一组技术的组合将给出更准确的分割结果。从分割算法中提取的掩码融合在一起,为外科医生提供3D可视化工具,以研究肝脏的空间关系并计算所需的切除规划参数。