Computer-assisted liver tumor surgery using a novel semiautomatic and a hybrid semiautomatic segmentation algorithm.
We developed a medical image segmentation and preoperative planning application which implements a semiautomatic and a hybrid semiautomatic liver segmentation algorithm. The aim of this study was to evaluate the feasibility of computer-assisted liver tumor surgery using these algorithms which are ba...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 54; no. 5; pp. 711 - 722 |
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| Autores principales: | , , , , , , , |
| Formato: | research randomized controlled trial Journal Article |
| Publicado: |
Springer Nature
May2016
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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=114606453&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 114606453 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2016 vid: 54 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 114606453 114606453 NLM26307199 114606453 10.1007/s11517-015-1369-5 NLM26307199 114606453 ppf: 711 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Computer-assisted liver tumor surgery using a novel semiautomatic and a hybrid semiautomatic segmentation algorithm. aug: au: Zygomalas, Apollon Karavias, Dionissios Koutsouris, Dimitrios Maroulis, Ioannis Karavias, Dimitrios Giokas, Konstantinos Megalooikonomou, Vasileios Karavias, Dimitrios D affil: Hepatobiliary and Pancreatic Unit, Department of Surgery, University Hospital of Patras, 26500 Patras Greece sug: subj: Surgery, Computer-Assisted Algorithms Liver Neoplasms Surgery Imaging, Three-Dimensional Female Middle Age Liver Neoplasms Male Liver Surgery Reproducibility of Results Body Weights and Measures Liver Pathology Adult Aged Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Randomized Controlled Trials Middle Aged: 45-64 years Adult: 19-44 years Aged: 65+ years Female Male ab: We developed a medical image segmentation and preoperative planning application which implements a semiautomatic and a hybrid semiautomatic liver segmentation algorithm. The aim of this study was to evaluate the feasibility of computer-assisted liver tumor surgery using these algorithms which are based on thresholding by pixel intensity value from initial seed points. A random sample of 12 patients undergoing elective high-risk hepatectomies at our institution was prospectively selected to undergo computer-assisted surgery using our algorithms (June 2013-July 2014). Quantitative and qualitative evaluation was performed. The average computer analysis time (segmentation, resection planning, volumetry, visualization) was 45 min/dataset. The runtime for the semiautomatic algorithm was <0.2 s/slice. Liver volumetric segmentation using the hybrid method was achieved in 12.9 s/dataset (SD ± 6.14). Mean similarity index was 96.2 % (SD ± 1.6). The future liver remnant volume calculated by the application showed a correlation of 0.99 to that calculated using manual boundary tracing. The 3D liver models and the virtual liver resections had an acceptable coincidence with the real intraoperative findings. The patient-specific 3D models produced using our semiautomatic and hybrid semiautomatic segmentation algorithms proved to be accurate for the preoperative planning in liver tumor surgery and effectively enhanced the intraoperative medical image guidance. pubtype: Academic Journal doctype: research randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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