Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning.
An innovative algorithm has been developed for the segmentation of retroperitoneal tumors in 3D radiological images. This algorithm makes it possible for radiation oncologists and surgeons semiautomatically to select tumors for possible future radiation treatment and surgery. It is based on continuo...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 1; pp. 1 - 16 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120629449&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120629449 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2017 vid: 55 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 120629449 120629449 NLM27099157 10.1007/s11517-016-1505-x NLM27099157 120629449 ppf: 1 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning. aug: au: Suárez-Mejías, Cristina Pérez-Carrasco, Jose Serrano, Carmen López-Guerra, Jose Parra-Calderón, Carlos Gómez-Cía, Tomás Acha, Begoña Suárez-Mejías, Cristina Pérez-Carrasco, Jose Antonio López-Guerra, Jose Luis Parra-Calderón, Carlos Gómez-Cía, Tomás Acha, Begoña affil: Signal Theory and Communications Department , University of Seville , Seville Spain sug: subj: Retroperitoneal Neoplasms Radiotherapy, Computer-Assisted Retroperitoneal Neoplasms Radiotherapy Imaging, Three-Dimensional Algorithms Adolescence Observer Bias Young Adult Female Linear Regression Adult Male Adolescent: 13-18 years Adult: 19-44 years Female Male ab: An innovative algorithm has been developed for the segmentation of retroperitoneal tumors in 3D radiological images. This algorithm makes it possible for radiation oncologists and surgeons semiautomatically to select tumors for possible future radiation treatment and surgery. It is based on continuous convex relaxation methodology, the main novelty being the introduction of accumulated gradient distance, with intensity and gradient information being incorporated into the segmentation process. The algorithm was used to segment 26 CT image volumes. The results were compared with manual contouring of the same tumors. The proposed algorithm achieved 90 % sensitivity, 100 % specificity and 84 % positive predictive value, obtaining a mean distance to the closest point of 3.20 pixels. The algorithm's dependence on the initial manual contour was also analyzed, with results showing that the algorithm substantially reduced the variability of the manual segmentation carried out by different specialists. The algorithm was also compared with four benchmark algorithms (thresholding, edge-based level-set, region-based level-set and continuous max-flow with two labels). To the best of our knowledge, this is the first time the segmentation of retroperitoneal tumors for radiotherapy planning has been addressed. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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