Liver segmentation on CT and MR using laplacian mesh optimization
dc.contributor.author | Chartrand, Gabriel | |
dc.contributor.author | Cresson, Thierry | |
dc.contributor.author | Chav, Ramnada | |
dc.contributor.author | Gotra, Akshat | |
dc.contributor.author | Tang, An | |
dc.contributor.author | De Guise, Jacques | |
dc.date.accessioned | 2023-07-03T12:14:56Z | |
dc.date.available | NO_RESTRICTION | fr |
dc.date.available | 2023-07-03T12:14:56Z | |
dc.date.issued | 2016-11-18 | |
dc.identifier.uri | http://hdl.handle.net/1866/28308 | |
dc.publisher | Institute of electrical and electronics engineers | fr |
dc.subject | Three-dimensional (3-D) | fr |
dc.subject | CT | fr |
dc.subject | Laplacian mesh optimization | fr |
dc.subject | Liver | fr |
dc.subject | MRI | fr |
dc.subject | Segmentation | fr |
dc.title | Liver segmentation on CT and MR using laplacian mesh optimization | fr |
dc.type | Article | fr |
dc.contributor.affiliation | Université de Montréal. Faculté de médecine. Département de radiologie, radio-oncologie et médecine nucléaire | fr |
dc.identifier.doi | 10.1109/TBME.2016.2631139 | |
dcterms.abstract | Objective: The purpose of this paper is to describe a semiautomated segmentation method for the liver and evaluate its performance on CT-scan and MR images. Methods: First, an approximate 3-D model of the liver is initialized from a few user-generated contours to globally outline the liver shape. The model is then automatically deformed by a Laplacian mesh optimization scheme until it precisely delineates the patient’s liver. A correction tool was implemented to allow the user to improve the segmentation until satisfaction. Results: The proposed method was tested against 30 CT-scans from the SLIVER07 challenge repository and 20 MR studies from the Montreal University Hospital Center, covering a wide spectrum of liver morphologies and pathologies. The average volumetric overlap error was 5.1% for CT and 7.6% for MRI and the average segmentation time was 6 min. Conclusion: The obtained results show that the proposed method is efficient, reliable, and could effectively be used routinely in the clinical setting. Significance: The proposed approach can alleviate the cumbersome and tedious process of slice-wise segmentation required for precise hepatic volumetry, virtual surgery, and treatment planning. | fr |
dcterms.isPartOf | urn:ISSN:0018-9294 | fr |
dcterms.isPartOf | urn:ISSN:1558-2531 | fr |
dcterms.language | eng | fr |
UdeM.ReferenceFournieParDeposant | Chartrand, G., Cresson, T., Chav, R., Gotra, A., Tang, A., & De Guise, J. A. (2017). Liver Segmentation on CT and MR Using Laplacian Mesh Optimization. IEEE transactions on bio-medical engineering, 64(9), 2110–2121. https://doi.org/10.1109/TBME.2016.2631139 | fr |
UdeM.VersionRioxx | Version acceptée / Accepted Manuscript | fr |
oaire.citationTitle | IEEE transactions on biomedical engineering | fr |
oaire.citationVolume | 64 | fr |
oaire.citationIssue | 9 | fr |
oaire.citationStartPage | 2110 | fr |
oaire.citationEndPage | 2121 | fr |
Files in this item
This item appears in the following Collection(s)
This document disseminated on Papyrus is the exclusive property of the copyright holders and is protected by the Copyright Act (R.S.C. 1985, c. C-42). It may be used for fair dealing and non-commercial purposes, for private study or research, criticism and review as provided by law. For any other use, written authorization from the copyright holders is required.