Gross Tumor Volume (GTV) segmentation on medical images is an open issue in neuro-radiosurgery. Magnetic Resonance Imaging (MRI) is the most promi-nent 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 op-erator-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 Au-tomata, 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.

Neuro-Radiosurgery Treatments: MRI Brain Tumor Seeded Image Segmentation Based on a Cellular Automata Model

Rundo Leonardo;Militello Carmelo;Russo Giorgio;Gilardi Maria Carla;
2016

Abstract

Gross Tumor Volume (GTV) segmentation on medical images is an open issue in neuro-radiosurgery. Magnetic Resonance Imaging (MRI) is the most promi-nent 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 op-erator-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 Au-tomata, 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.
2016
Istituto di Bioimmagini e Fisiologia Molecolare - IBFM
978-3-319-44364-5
Gamma Knife treatments
MR imaging
Brain tumors
Cellular Automata
Semi-automatic segmentation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/330063
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