Title
GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model.
Abstract
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
Venue
Field
2018
Natural Computing
Cellular automaton,Pattern recognition,Segmentation,Radiation treatment planning,Radiosurgery,Image segmentation,Radiation therapy,Artificial intelligence,Mathematics,Machine learning,Cancer,Magnetic resonance imaging
DocType
Volume
Issue
Journal
17
3
Citations 
PageRank 
References 
0
0.34
24
Authors
6
Name
Order
Citations
PageRank
Leonardo Rundo1256.40
Carmelo Militello211211.68
Giorgio Russo3113.65
S. Vitabile46510.29
Maria Carla Gilardi5589.92
Giancarlo Mauri62106297.38