http://www.cnr.it/ontology/cnr/individuo/prodotto/ID186209
Hepatic vessel segmentation for 3D planning of liver surgery: experimental evaluation of a new fully automatic algorithm (Articolo in rivista)
- Type
- Label
- Hepatic vessel segmentation for 3D planning of liver surgery: experimental evaluation of a new fully automatic algorithm (Articolo in rivista) (literal)
- Anno
- 2011-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1016/j.acra.2010.11.015 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Conversano F.; Franchini R.; Demitri C.; Massoptier L.; Montagna F.; Maffezzoli A.; Malvasi A.; Casciaro S. (literal)
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- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Biomedical Engineering, Science and Technology Division, Institute of Clinical Physiology, National Research Council; Department of Engineering for Innovation, University of Salento; Department of Obstetrics and Gynecology, Santa Maria Hospital. (literal)
- Titolo
- Hepatic vessel segmentation for 3D planning of liver surgery: experimental evaluation of a new fully automatic algorithm (literal)
- Abstract
- Rationale and Objectives: The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic
segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver
surgery.
Materials and Methods: A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to
a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic
data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy
was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the
automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features.
Results: The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the
range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and
manually segmented vessel sections, with an average difference of 0.06 mm2. The average values of corresponding false-positive and
false-negative ratios were 7.7% and 2.3%, respectively.
Conclusions: A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed
tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel
delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative
resections. (literal)
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