6533b871fe1ef96bd12d2579
RESEARCH PRODUCT
GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model
Leonardo Rundo2Carmelo MilitelloGiorgio Russo 234Salvatore Vitabile 5Maria Carla Gilardi 2Giancarlo Maurisubject
Cellular automataBrain cancersING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICABrain cancers; Cellular automata; Computer-assisted segmentation; Gamma Knife neuro-radiosurgery; MR imagingComputer sciencemedicine.medical_treatment02 engineering and technologyBrain cancerRadiosurgeryING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicine0202 electrical engineering electronic engineering information engineeringmedicineSegmentationRadiation treatment planningModality (human–computer interaction)medicine.diagnostic_testbusiness.industryComputer Science ApplicationComputer-assisted segmentationINF/01 - INFORMATICAMagnetic resonance imagingPattern recognitionGamma Knife neuro-radiosurgeryComputer Science Applications1707 Computer Vision and Pattern RecognitionImage segmentationCellular automatonComputer Science ApplicationsRadiation therapy020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessMR imagingdescription
Despite of the development of advanced segmentation techniques, achieving accurate and reproducible gross tumor volume (GTV) segmentation results is still an important challenge in neuro-radiosurgery. Nowadays, magnetic resonance imaging (MRI) is the most prominent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a minimally invasive technology for dealing with inaccessible or insufficiently treated tumors with traditional surgery or radiotherapy. During a treatment planning phase, the GTV is generally contoured by experienced neurosurgeons and radiation oncologists using fully manual segmentation procedures on MR images. Unfortunately, this operative methodology is definitely time-expensive and operator-dependent. Delineation result repeatability, in terms of both intra- and inter-operator reliability, can be achieved only by using computer-assisted approaches. In this paper a novel semi-automatic seeded image segmentation method, based on a cellular automata model, for MRI brain cancer detection and delineation is proposed. This approach, called GTVcut, employs an adaptive seed selection strategy and helps to segment the GTV, by identifying the target volume to be treated using the Gamma Knife device. The accuracy of GTVcut was evaluated on a dataset composed of 32 brain cancers, using both spatial overlap-based and distance-based metrics. The achieved experimental results are very reproducible, showing the effectiveness and the clinical feasibility of the proposed approach.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-01-01 |