An automatic method for metabolic evaluation of gamma knife treatments

Salvatore Vitabile; Orazio Gambino; Edoardo Ardizzone; Alessandro Stefano; Roberto Pirrone; Maria Carla Gilardi; Alessandro Stefano; Giorgio Russo; Massimo Ippolito; Corrado D’Arrigo; Davide D’Urso; Franco Marletta; Maria Gabriella Sabini

An automatic method for metabolic evaluation of gamma knife treatments

Salvatore Vitabile, Orazio Gambino, Edoardo Ardizzone, Alessandro Stefano, Roberto Pirrone, Maria Carla Gilardi, Alessandro Stefano, Giorgio Russo, Massimo Ippolito, Corrado D’Arrigo, Davide D’Urso, Franco Marletta, Maria Gabriella Sabini

Risultato della ricerca: Chapter

9 Citazioni (Scopus)

Abstract

Lesion volume delineation of Positron Emission Tomography images is challenging because of the low spatial resolution and high noise level. Aim of this work is the development of an operator independent segmentation method of metabolic images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Twenty-four cerebral tumors are segmented to evaluate the functional follow-up after Gamma Knife radiotherapy treatment. Experimental results show that the segmentation algorithm is accurate and has real-time performance. In addition, it can reflect metabolic changes useful to evaluate radiotherapy response in treated patients.

Lingua originale	English
Titolo della pubblicazione ospite	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pagine	579-589
Numero di pagine	11
Stato di pubblicazione	Published - 2015

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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Vitabile, S., Gambino, O., Ardizzone, E., Stefano, A., Pirrone, R., Gilardi, M. C., Stefano, A., Russo, G., Ippolito, M., D’Arrigo, C., D’Urso, D., Marletta, F., & Sabini, M. G. (2015). An automatic method for metabolic evaluation of gamma knife treatments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pagg. 579-589)

An automatic method for metabolic evaluation of gamma knife treatments. / Vitabile, Salvatore ; Gambino, Orazio ; Ardizzone, Edoardo ; Stefano, Alessandro ; Pirrone, Roberto; Gilardi, Maria Carla; Stefano, Alessandro; Russo, Giorgio; Ippolito, Massimo; D’Arrigo, Corrado; D’Urso, Davide; Marletta, Franco; Sabini, Maria Gabriella.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. pag. 579-589.

Risultato della ricerca: Chapter

Vitabile, S , Gambino, O , Ardizzone, E , Stefano, A , Pirrone, R, Gilardi, MC, Stefano, A, Russo, G, Ippolito, M, D’Arrigo, C, D’Urso, D, Marletta, F & Sabini, MG 2015, An automatic method for metabolic evaluation of gamma knife treatments. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pagg. 579-589.

Vitabile, Salvatore ; Gambino, Orazio ; Ardizzone, Edoardo ; Stefano, Alessandro ; Pirrone, Roberto ; Gilardi, Maria Carla ; Stefano, Alessandro ; Russo, Giorgio ; Ippolito, Massimo ; D’Arrigo, Corrado ; D’Urso, Davide ; Marletta, Franco ; Sabini, Maria Gabriella. / An automatic method for metabolic evaluation of gamma knife treatments. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. pagg. 579-589

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AU - Vitabile, Salvatore

AU - Gambino, Orazio

AU - Ardizzone, Edoardo

AU - Stefano, Alessandro

AU - Pirrone, Roberto

AU - Gilardi, Maria Carla

AU - Stefano, Alessandro

AU - Russo, Giorgio

AU - Ippolito, Massimo

AU - D’Arrigo, Corrado

AU - D’Urso, Davide

AU - Marletta, Franco

AU - Sabini, Maria Gabriella

PY - 2015

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AB - Lesion volume delineation of Positron Emission Tomography images is challenging because of the low spatial resolution and high noise level. Aim of this work is the development of an operator independent segmentation method of metabolic images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Twenty-four cerebral tumors are segmented to evaluate the functional follow-up after Gamma Knife radiotherapy treatment. Experimental results show that the segmentation algorithm is accurate and has real-time performance. In addition, it can reflect metabolic changes useful to evaluate radiotherapy response in treated patients.

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KW - Computer Science (all)

KW - Gamma Knife treatment

KW - PET imaging

KW - Random walk

KW - Segmentation

KW - Theoretical Computer Science

KW - Biological target volume

KW - Computer Science (all)

KW - Gamma Knife treatment

KW - PET imaging

KW - Random walk

KW - Segmentation

KW - Theoretical Computer Science

UR - http://hdl.handle.net/10447/153964

M3 - Chapter

SN - 9783319232300

SP - 579

EP - 589

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -