title: Towards a Computer Aided Prognosis for Brain Glioblastomas Tumor Growth Estimation.
abstract: Bridging the gap between mathematical and biological models and clinical applications could be considered as one of the new challenges of medical image analysis over the ten last years. This paper presents an advanced and convivial algorithm for brain glioblastomas tumor growth modelization. The brain glioblastomas tumor region would be extracted using a fast distribution matching developed algorithm based on global pixel wise information. A new model to simulate the tumor growth based on two major elements: cellular automata and fast marching method (CFMM) has been developed and used to estimate the brain tumor evolution during the time. On the basis of this model, experiments were carried out on twenty pathological MRI selected cases that were carefully discussed with the clinical part. The obtained simulated results were validated with ground truth references (real tumor growth measure) using dice metric parameter. As carefully discussed with the clinical partner, experimental results showed that our proposed algorithm for brain glioblastomas tumor growth model proved a good agreement. Our main purpose behind this research was of course to make advances and progress during clinical explorations helping therefore radiologists in their diagnosis. Clinical decisions and guidelines would be hence so more focused with such an advanced tool that could help clinicians and ensuring more accuracy and objectivity.
authors.forename: Lamia
authors.initials: L
authors.lastname: Sallemi
authors.forename: Ines
authors.initials: I
authors.lastname: Njeh
authors.forename: Stephane
authors.initials: S
authors.lastname: Lehericy
mesh_terms: D001932:Brain Neoplasms / Q000473:pathology* / Q000503:physiopathology; D049109:Cell Proliferation; D003198:Computer Simulation; D005909:Glioblastoma / Q000473:pathology* / Q000503:physiopathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D009361:Neoplasm Invasiveness; D011379:Prognosis