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A fully automatic method for biological target volume segmentation of brain metastases
Author(s) -
Stefano Alessandro,
Vitabile Salvatore,
Russo Giorgio,
Ippolito Massimo,
Marletta Franco,
D'arrigo Corrado,
D'urso Davide,
Gambino Orazio,
Pirrone Roberto,
Ardizzone Edoardo,
Gilardi Maria Carla
Publication year - 2016
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22154
Subject(s) - segmentation , computer science , thresholding , partial volume , artificial intelligence , cluster analysis , positron emission tomography , volume (thermodynamics) , computer vision , fuzzy logic , pattern recognition (psychology) , nuclear medicine , image (mathematics) , medicine , physics , quantum mechanics
Leksell Gamma Knife is a mini‐invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator‐dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40% thresholding method, region growing method, and fuzzy c‐means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real‐time performance satisfying the physician requirements in a radiotherapy environment.