Neuro-radiosurgery treatments: MRI brain tumor seeded image segmentation based on a cellular automata model

Salvatore Vitabile, Maria Carla Gilardi, Giorgio Russo, Carmelo Militello, Pietro Pisciotta, Leonardo Rundo, Maria Gabriella Sabini, Giancarlo Mauri, Lucia Maria Valastro

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4 Citazioni (Scopus)

Abstract

Gross Tumor Volume (GTV) segmentation on medical images is an open issue in neuro-radiosurgery. Magnetic Resonance Imaging (MRI) is the most prominent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a mini-invasive technique used to deal with inaccessible or insufficiently treated tumors. During the planning phase, the GTV is usually contoured by radiation oncologists using a manual segmentation procedure on MR images. This methodology is certainly time-consuming and operator-dependent. Delineation result repeatability, in terms of both intra- and inter-operator reliability, is only obtained by using computer-assisted approaches. In this paper a novel semi-automatic segmentation method, based on Cellular Automata, is proposed. The developed approach allows for the GTV segmentation and computes the lesion volume to be treated. The method was evaluated on 10 brain cancers, using both area-based and distance-based metrics.
Lingua originaleEnglish
Titolo della pubblicazione ospiteCellular Automata
Pagine323-333
Numero di pagine11
Stato di pubblicazionePublished - 2016

Serie di pubblicazioni

NomeLECTURE NOTES IN COMPUTER SCIENCE

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cita questo

Vitabile, S., Gilardi, M. C., Russo, G., Militello, C., Pisciotta, P., Rundo, L., Sabini, M. G., Mauri, G., & Valastro, L. M. (2016). Neuro-radiosurgery treatments: MRI brain tumor seeded image segmentation based on a cellular automata model. In Cellular Automata (pagg. 323-333). (LECTURE NOTES IN COMPUTER SCIENCE).