Live minimal path for interactive segmentation of medical images
dc.contributor.author | Chartrand, Gabriel | |
dc.contributor.author | Tang, An | |
dc.contributor.author | Chav, Ramnada | |
dc.contributor.author | Cresson, Thierry | |
dc.contributor.author | Steeve, Chantrel | |
dc.contributor.author | De Guise, Jacques | |
dc.date.accessioned | 2023-06-20T18:46:50Z | |
dc.date.available | NO_RESTRICTION | fr |
dc.date.available | 2023-06-20T18:46:50Z | |
dc.date.issued | 2015-03-20 | |
dc.identifier.uri | http://hdl.handle.net/1866/28276 | |
dc.publisher | Society of photo-optical instrumentation engineers | fr |
dc.subject | Medical | fr |
dc.subject | Image | fr |
dc.subject | Segmentation | fr |
dc.subject | Interactive | fr |
dc.subject | Minimal path | fr |
dc.title | Live minimal path for interactive segmentation of medical images | 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 | 10.1117/12.2081453 | |
dcterms.abstract | Medical image segmentation is nowadays required for medical device development and in a growing number of clinical and research applications. Since dedicated automatic segmentation methods are not always available, generic and efficient interactive tools can alleviate the burden of manual segmentation. In this paper we propose an interactive segmentation tool based on image warping and minimal path segmentation that is efficient for a wide variety of segmentation tasks. While the user roughly delineates the desired organs boundary, a narrow band along the cursors path is straightened, providing an ideal subspace for feature aligned filtering and minimal path algorithm. Once the segmentation is performed on the narrow band, the path is warped back onto the original image, precisely delineating the desired structure. This tool was found to have a highly intuitive dynamic behavior. It is especially efficient against misleading edges and required only coarse interaction from the user to achieve good precision. The proposed segmentation method was tested for 10 difficult liver segmentations on CT and MRI images, and the resulting 2D overlap Dice coefficient was 99% on average. | fr |
dcterms.description | Medical Imaging 2015: Image Processing, 94133U (20 mars 2015) | fr |
dcterms.isPartOf | urn:ISSN:0277-786X | fr |
dcterms.isPartOf | urn:ISSN:1996-756X | fr |
dcterms.language | eng | fr |
UdeM.ReferenceFournieParDeposant | Chartrand, G., Tang, A., Chav, R., Cresson, T., Chantrel, S., & de Guise, J. (2015). Live minimal path for interactive segmentation of medical images. 9413. https://doi.org/10.1117/12.2081453 | fr |
UdeM.VersionRioxx | Version acceptée / Accepted Manuscript | fr |
oaire.citationTitle | Proceedings of SPIE | fr |
oaire.citationVolume | 9413 | fr |
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