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 |
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