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A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities
Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS, SIGMA Clermont,Inst Pascal, F-63000 Clermont Ferrand, France.
Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS, SIGMA Clermont,Inst Pascal, F-63000 Clermont Ferrand, France.
Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS, SIGMA Clermont,Inst Pascal, F-63000 Clermont Ferrand, France.
Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS, SIGMA Clermont,Inst Pascal, F-63000 Clermont Ferrand, France.
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2019 (English)In: Computerized Medical Imaging and Graphics, ISSN 0895-6111, E-ISSN 1879-0771, Vol. 76, article id UNSP 101635Article in journal (Refereed) Published
Abstract [en]

Developing methods to segment the liver in medical images, study and analyze it remains a significant challenge. The shape of the liver can vary considerably from one patient to another, and adjacent organs are visualized in medical images with similar intensities, making the boundaries of the liver ambiguous. Consequently, automatic or semi-automatic segmentation of liver is a difficult task. Moreover, scanning systems and magnetic resonance imaging have different settings and parameters. Thus the images obtained differ from one machine to another. In this article, we propose an automatic model-based segmentation that allows building a faithful 3-D representation of the liver, with a mean Dice value equal to 90.3% on CT and MRI datasets. We compare our algorithm with a semi-automatic method and with other approaches according to the state of the art. Our method works with different data sources, we use a large quantity of CT and MRI images from machines in various hospitals and multiple DICOM images available from public challenges. Finally, for evaluation of liver segmentation approaches in state of the art, robustness is not adequacy addressed with a precise definition. Another originality of this article is the introduction of a novel measure of robustness, which takes into account the liver variability at different scales. (C) 2019 Published by Elsevier Ltd.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2019. Vol. 76, article id UNSP 101635
Keywords [en]
Automatic segmentation, 3-D, Liver, CT, MRI, Shape model, Variability, Robustness
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-396624DOI: 10.1016/j.compmedimag.2019.05.003ISI: 000490629700004PubMedID: 31301489OAI: oai:DiVA.org:uu-396624DiVA, id: diva2:1369915
Available from: 2019-11-13 Created: 2019-11-13 Last updated: 2019-11-13Bibliographically approved

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Strand, Robin

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