Literature DB >> 18003190

Fully automatic liver segmentation through graph-cut technique.

Laurent Massoptier1, Sergio Casciaro.   

Abstract

The accurate knowledge of the liver structure including blood vessels topography, liver surface and lesion localizations is usually required in treatments like liver ablations and radiotherapy. In this paper, we propose an approach for automatic segmentation of liver complex geometries. It consists of applying a graph-cut method initialized by an adaptive threshold. The algorithm has been tested on 10 datasets (CT and MR). A parametric comparison with the results obtained by previous algorithms based on active contour is also carried out and discussed. Main limitations of active contour approaches result to be overcome and segmentation is improved. Feasibility to routinely use graph-cut approach for automatic liver segmentation is also demonstrated.

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Year:  2007        PMID: 18003190     DOI: 10.1109/IEMBS.2007.4353524

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  13 in total

1.  Graph-cut energy minimization for object extraction in MRCP medical images.

Authors:  Rajasvaran Logeswaran; Dongho Kim; Jungwhan Kim; Keechul Jung; Bundo Song
Journal:  J Med Syst       Date:  2010-04-06       Impact factor: 4.460

2.  Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors.

Authors:  Toshiyuki Okada; Marius George Linguraru; Masatoshi Hori; Ronald M Summers; Noriyuki Tomiyama; Yoshinobu Sato
Journal:  Med Image Anal       Date:  2015-07-04       Impact factor: 8.545

3.  Automatic 3D liver location and segmentation via convolutional neural network and graph cut.

Authors:  Fang Lu; Fa Wu; Peijun Hu; Zhiyi Peng; Dexing Kong
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-09-07       Impact factor: 2.924

4.  A Lightweight Convolutional Neural Network Model for Liver Segmentation in Medical Diagnosis.

Authors:  Mubashir Ahmad; Syed Furqan Qadri; Salman Qadri; Iftikhar Ahmed Saeed; Syeda Shamaila Zareen; Zafar Iqbal; Amerah Alabrah; Hayat Mansoor Alaghbari; Sk Md Mizanur Rahman
Journal:  Comput Intell Neurosci       Date:  2022-03-30

5.  Adapting liver motion models using a navigator channel technique.

Authors:  T N Nguyen; J L Moseley; L A Dawson; D A Jaffray; K K Brock
Journal:  Med Phys       Date:  2009-04       Impact factor: 4.071

6.  Multi-organ segmentation in abdominal CT images.

Authors:  Toshiyuki Okada; Marius George Linguraru; Masatoshi Hori; Yuki Suzuki; Ronald M Summers; Noriyuki Tomiyama; Yoshinobu Sato
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2012

7.  Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation.

Authors:  Shuiping Gou; Percy Lee; Peng Hu; Jean-Claude Rwigema; Ke Sheng
Journal:  Adv Radiat Oncol       Date:  2016-05-30

8.  Functional Region Annotation of Liver CT Image Based on Vascular Tree.

Authors:  Yufei Chen; Xiaodong Yue; Caiming Zhong; Gang Wang
Journal:  Biomed Res Int       Date:  2016-11-07       Impact factor: 3.411

9.  Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts.

Authors:  Weiwei Wu; Zhuhuang Zhou; Shuicai Wu; Yanhua Zhang
Journal:  Comput Math Methods Med       Date:  2016-04-05       Impact factor: 2.238

10.  Deep Learning Renal Segmentation for Fully Automated Radiation Dose Estimation in Unsealed Source Therapy.

Authors:  Price Jackson; Nicholas Hardcastle; Noel Dawe; Tomas Kron; Michael S Hofman; Rodney J Hicks
Journal:  Front Oncol       Date:  2018-06-14       Impact factor: 6.244

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