GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model

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...

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Bibliographic Details
Published in:Natural computing 2018-09, Vol.17 (3), p.521-536
Main Authors: Rundo, Leonardo, Militello, Carmelo, Russo, Giorgio, Vitabile, Salvatore, Gilardi, Maria Carla, Mauri, Giancarlo
Format: Article
Language:English
Subjects:
Artificial Intelligence
Brain
Brain cancer
Cancer
Cellular automata
Complex Systems
Computer Science
Delineation
Evolutionary Biology
Image segmentation
Magnetic resonance imaging
Medical imaging
NMR
Nuclear magnetic resonance
Processor Architectures
Radiation therapy
Radiology
Surgery
Theory of Computation
Time dependence
Tumors
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Summary: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 GTV cut , 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 GTV cut 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.
ISSN:1567-7818
1572-9796
DOI:10.1007/s11047-017-9636-z