Literature DB >> 19059767

Liver segmentation from computed tomography scans: a survey and a new algorithm.

Paola Campadelli1, Elena Casiraghi, Andrea Esposito.   

Abstract

OBJECTIVE: In the recent years liver segmentation from computed tomography scans has gained a lot of importance in the field of medical image processing since it is the first and fundamental step of any automated technique for the automatic liver disease diagnosis, liver volume measurement, and 3D liver volume rendering.
METHODS: In this paper we report a review study about the semi-automatic and automatic liver segmentation techniques, and we describe our fully automatized method.
RESULTS: The survey reveals that automatic liver segmentation is still an open problem since various weaknesses and drawbacks of the proposed works must still be addressed. Our gray-level based liver segmentation method has been developed to tackle all these problems; when tested on 40 patients it achieves satisfactory results, comparable to the mean intra- and inter-observer variation.
CONCLUSIONS: We believe that our technique outperforms those presented in the literature; nevertheless, a common test set with its gold standard traced by experts, and a generally accepted performance measure are required to demonstrate it.

Entities:  

Mesh:

Year:  2008        PMID: 19059767     DOI: 10.1016/j.artmed.2008.07.020

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  37 in total

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2.  Treatment planning and volumetric response assessment for Yttrium-90 radioembolization: semiautomated determination of liver volume and volume of tumor necrosis in patients with hepatic malignancy.

Authors:  Wayne L Monsky; Armando S Garza; Isaac Kim; Shaun Loh; Tzu-Chun Lin; Chin-Shang Li; Jerron Fisher; Parmbir Sandhu; Vishal Sidhar; Abhijit J Chaudhari; Frank Lin; Larry-Stuart Deutsch; Ramsey D Badawi
Journal:  Cardiovasc Intervent Radiol       Date:  2010-08-04       Impact factor: 2.740

3.  Iterative mesh transformation for 3D segmentation of livers with cancers in CT images.

Authors:  Difei Lu; Yin Wu; Gordon Harris; Wenli Cai
Journal:  Comput Med Imaging Graph       Date:  2015-01-28       Impact factor: 4.790

4.  Ray-casting based evaluation framework for haptic force feedback during percutaneous transhepatic catheter drainage punctures.

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Review 5.  Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters.

Authors:  Omar Ibrahim Alirr; Ashrani Aizzuddin Abd Rahni
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6.  The use of nano-computed tomography to enhance musculoskeletal research.

Authors:  Basma M Khoury; Erin M R Bigelow; Lauren M Smith; Stephen H Schlecht; Erica L Scheller; Nelly Andarawis-Puri; Karl J Jepsen
Journal:  Connect Tissue Res       Date:  2015-02-03       Impact factor: 3.417

7.  Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation.

Authors:  Marius George Linguraru; Jesse K Sandberg; Zhixi Li; Furhawn Shah; Ronald M Summers
Journal:  Med Phys       Date:  2010-02       Impact factor: 4.071

8.  Computer aided preoperative evaluation of the residual liver volume using computed tomography images.

Authors:  Kristina Bliznakova; Nikola Kolev; Ivan Buliev; Anton Tonev; Elitsa Encheva; Zhivko Bliznakov; Krasimir Ivanov
Journal:  J Digit Imaging       Date:  2015-04       Impact factor: 4.056

9.  Automated measurement of liver attenuation to identify moderate-to-severe hepatic steatosis from chest CT scans.

Authors:  Artit Jirapatnakul; Anthony P Reeves; Sara Lewis; Xiangmeng Chen; Teng Ma; Rowena Yip; Xing Chin; Shuang Liu; Ponni V Perumalswami; David F Yankelevitz; Michael Crane; Andrea D Branch; Claudia I Henschke
Journal:  Eur J Radiol       Date:  2019-10-25       Impact factor: 3.528

10.  Incorporating prior shape knowledge via data-driven loss model to improve 3D liver segmentation in deep CNNs.

Authors:  Saeed Mohagheghi; Amir Hossein Foruzan
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-11-04       Impact factor: 2.924

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