6533b828fe1ef96bd1289029
RESEARCH PRODUCT
Neuro-radiosurgery treatments: MRI brain tumor seeded image segmentation based on a cellular automata model
Maria Gabriella SabiniSalvatore VitabileL. M. ValastroCarmelo MilitelloMaria Carla GilardiGiancarlo MauriGiorgio Ivan RussoLeonardo RundoPietro PisciottaPietro Pisciottasubject
medicine.medical_specialtyComputer sciencemedicine.medical_treatment02 engineering and technologyCellular AutomataBrain tumors; Cellular automata; Gamma knife treatments; MR imaging; Semi-automatic segmentationBrain tumorsRadiosurgery030218 nuclear medicine & medical imagingTheoretical Computer Science03 medical and health sciences0302 clinical medicineGamma Knife treatments0202 electrical engineering electronic engineering information engineeringmedicineSegmentationMri brainModality (human–computer interaction)medicine.diagnostic_testSemi-automatic segmentationbusiness.industryINF/01 - INFORMATICAMagnetic resonance imagingImage segmentationCellular automatonRadiation therapyBrain tumor020201 artificial intelligence & image processingGamma Knife treatmentArtificial intelligenceRadiologybusinessMR imagingdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 |