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      Liver segmentation: indications, techniques and future directions

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          Abstract

          Objectives

          Liver volumetry has emerged as an important tool in clinical practice. Liver volume is assessed primarily via organ segmentation of computed tomography (CT) and magnetic resonance imaging (MRI) images. The goal of this paper is to provide an accessible overview of liver segmentation targeted at radiologists and other healthcare professionals.

          Methods

          Using images from CT and MRI, this paper reviews the indications for liver segmentation, technical approaches used in segmentation software and the developing roles of liver segmentation in clinical practice.

          Results

          Liver segmentation for volumetric assessment is indicated prior to major hepatectomy, portal vein embolisation, associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) and transplant. Segmentation software can be categorised according to amount of user input involved: manual, semi-automated and fully automated. Manual segmentation is considered the “gold standard” in clinical practice and research, but is tedious and time-consuming. Increasingly automated segmentation approaches are more robust, but may suffer from certain segmentation pitfalls. Emerging applications of segmentation include surgical planning and integration with MRI-based biomarkers.

          Conclusions

          Liver segmentation has multiple clinical applications and is expanding in scope. Clinicians can employ semi-automated or fully automated segmentation options to more efficiently integrate volumetry into clinical practice.

          Teaching points

          Liver volume is assessed via organ segmentation on CT and MRI examinations.

          Liver segmentation is used for volume assessment prior to major hepatic procedures.

          Segmentation approaches may be categorised according to the amount of user input involved.

          Emerging applications include surgical planning and integration with MRI-based biomarkers.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s13244-017-0558-1) contains supplementary material, which is available to authorised users.

          Related collections

          Most cited references53

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          Comparison and evaluation of methods for liver segmentation from CT datasets.

          This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
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            Is Open Access

            Automated medical image segmentation techniques

            Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.
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              Improving resectability of hepatic colorectal metastases: expert consensus statement.

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                Author and article information

                Contributors
                (514) 890-8000 , an.tang@umontreal.ca
                Journal
                Insights Imaging
                Insights Imaging
                Insights into Imaging
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1869-4101
                14 June 2017
                14 June 2017
                August 2017
                : 8
                : 4
                : 377-392
                Affiliations
                [1 ]ISNI 0000 0001 2292 3357, GRID grid.14848.31, Department of Radiology, Radio-oncology and Nuclear Medicine, , University of Montreal, Saint-Luc Hospital, ; 1058 rue Saint-Denis, Montreal, QC H2X 3J4 Canada
                [2 ]ISNI 0000 0004 1936 8649, GRID grid.14709.3b, Department of Radiology, , McGill University, Montreal General Hospital, ; 1650 Cedar Avenue, Montreal, QC H3G 1A4 Canada
                [3 ]ISNI 0000 0001 2292 3357, GRID grid.14848.31, , University of Montreal, ; 2900 boulevard Eduoard-Montpetit, Montreal, QC H3T 1J4 Canada
                [4 ]ISNI 0000 0001 0743 2111, GRID grid.410559.c, , Centre de recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), ; 900 rue Saint-Denis, Montreal, QC H2X 0A9 Canada
                [5 ]ISNI 0000 0001 0743 2111, GRID grid.410559.c, Imaging and Orthopaedics Research Laboratory (LIO), École de technologie supérieure, , Centre de recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), ; 900 rue Saint-Denis, Montreal, QC H2X 0A9 Canada
                [6 ]ISNI 0000 0001 2292 3357, GRID grid.14848.31, Department of Hepato-biliary and Pancreatic Surgery, , University of Montreal, Saint-Luc Hospital, ; 1058 rue Saint-Denis, Montreal, QC H2X 3J4 Canada
                [7 ]ISNI 0000 0001 2292 3357, GRID grid.14848.31, École Polytechnique de Montréal, , University of Montreal, ; 2500 chemin de Polytechnique Montréal, Montreal, QC H3T 1J4 Canada
                Article
                558
                10.1007/s13244-017-0558-1
                5519497
                28616760
                edc943c9-b770-4db4-bac3-e6cf61b039ca
                © The Author(s) 2017

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 8 January 2017
                : 3 April 2017
                : 2 May 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000035, Institute of Nutrition, Metabolism and Diabetes;
                Award ID: 273738
                Award Recipient :
                Funded by: Réseau en Bio-Imagerie du Quebec (CA)
                Award ID: 8436-0501
                Award Recipient :
                Funded by: Centre de recherche du Centre hospitalier de l’Université de Montréal
                Funded by: Fonds de Recherche du Québec-Société et Culture - Association de radiologistes du Québec (CA)
                Award ID: 26993
                Award Recipient :
                Funded by: Mitacs (CA)
                Award ID: IT02111
                Award Recipient :
                Funded by: Canada Research Chairs (CA)
                Categories
                Review
                Custom metadata
                © The Author(s) 2017

                Radiology & Imaging
                liver,segmentation,volumetry,automated,computed tomography,magnetic resonance imaging

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